Automated Generation And Use Of Building Videos Based On Analysis Of Building Floor Plan Information

Techniques are described for using computing devices to perform automated operations for automatically generating videos and associated information about a building interior using other visual data about the building interior, as well as presenting the generated videos and associated information in various manners. In some situations, the generation is based at least in part on user input provided via user interactions with a displayed floor plan of the building, such as to select one or more rooms or other areas for which to include visual data in the video, and/or to select one or more building objects and/or other building structural elements and/or other building attributes for which to include visual data in the video. The techniques may further include determining and using information about such building attributes of the building from automated analysis of building information that includes floor plans and acquired building images.

TECHNICAL FIELD

The following disclosure relates generally to techniques for automatically generating building videos based on automated analysis of acquired building information that includes building images and floor plans, and for automatically using such generated building videos in further manners, such as for improved identification and navigation of buildings.

BACKGROUND

In various circumstances, such as architectural analysis, property inspection, real estate acquisition and development, general contracting, improvement cost estimation, etc., it may be desirable to know the interior of a house or other building without physically traveling to and entering the building. However, it can be difficult to effectively capture, represent and use such building interior information, including to identify buildings that satisfy criteria of interest, and to display visual information captured within building interiors to users at remote locations (e.g., to enable a user to understand the layout and other details of the interior, including to control the display in user-selected manners). Also, while a floor plan of a building may provide some information about layout and other details of a building interior, such use of floor plans has some drawbacks, including that floor plans can be difficult to construct and maintain, to accurately scale and populate with information about room interiors, to visualize and otherwise use, etc. Also, while textual descriptions of buildings may sometimes exist, they are often inaccurate and/or incomplete (e.g., lack details about various attributes of the buildings, include incorrect or misleading information, etc.).

DETAILED DESCRIPTION

The present disclosure describes techniques for using computing devices to perform automated operations involving generating videos about buildings from analysis of acquired building images and building floor plans and optionally other building information, such as for a building interior based on images acquired within the building and on a floor plan showing structural elements of the building interior, and to subsequently using the generated videos in one or more further automated manners, such as for improved identification and navigation of buildings. The automated techniques may include using information about objects (e.g., structural elements) and other attributes of a building as part of the video generation (e.g., to select aspects of one or more rooms or other areas to highlight), such as from automated analysis of information about the building (e.g., acquired images for the building, floor plans, etc.), and in some cases automated generation of textual descriptions about the determined building attributes, such as by using one or more trained machine learning models (e.g., trained neural networks) and/or one or more trained language models (e.g., large language models)—such building information may, in at least some embodiments, be for an as-built multi-room building (e.g., a house, office building, etc.) and include panorama images (e.g., with 360° of horizontal video coverage) and/or other images (e.g., rectilinear perspective images) acquired at acquisition locations in and around the building (e.g., without having or using information from any depth sensors or other distance-measuring devices about distances from an image's acquisition location to walls or other objects in the surrounding building). In some cases, the automated techniques may further include using the generated videos in various manners, such as to assist in determining buildings that match specified criteria, for controlling navigation of mobile devices (e.g., autonomous vehicles), for display or other presentation on client device(s) in corresponding GUIs (graphical user interfaces) to enable virtual navigation of a building, etc. Additional details are included below regarding automated generation and use of video information about buildings from automated analysis of building information, and some or all techniques described herein may, in at least some embodiments, be performed via automated operations of a Building Video Generation and Usage Manager (“BVGUM”) system, as discussed further below.

Automated operations of a BVGUM system may in at least some embodiments include obtaining one or more existing videos that each has visual data covering at least some of a building (e.g., multiple rooms of the building and/or both interior and exterior portions of the building), and automatically generating one or more additional videos for parts of the building from at least one of the existing videos, such as by identifying one or more subset segments of at least one existing video that satisfy one or more defined segment criteria and using the subset segment(s) as some or all of a new generated video. The identifying of one or more subset segments of at least one existing video that satisfy one or more defined segment criteria may include, for example, analyzing some or all frames of the existing video to identify one or more corresponding types of building information, such as a room or other area for which a frame includes visual data (e.g., a room or other area in which the camera that captured the video was located at a time of capturing that frame), and/or one or more objects or building structural elements or other building attributes shown in the visual data of the frame, and/or locations in the video of transitions between rooms and/or other areas (e.g., passing through doorways or non-doorway wall openings), and/or by localizing some or all of the existing video to a building floor plan by determining a path through some or all of the building that the camera followed while acquiring the video (e.g., using one or more of SfM (Structure from Motion) or SLAM (Simultaneous Location And Mapping) or MVS (Multi-View Stereo) analysis), and/or detecting movement patterns within such a path that satisfy one or more defined movement criteria (e.g., detecting a room or other area of interest based on the path entering that room or other area, turning in a circle, and then leaving in substantially the same direction as the entering). Such objects or building structural elements or other building attributes may have various forms and may be identified from analysis of a frame's visual data in various manners, as discussed further below, with non-exclusive examples of such building attributes including windows, doorways, non-doorway wall opening, walls and ceilings and floors and borders between at least two of them, built-in or movable objects, etc., In addition, the identification of a room or other area for which a frame includes visual data may be performed in various manners in various embodiments, with non-exclusive examples including the following: comparing the visual data of a frame to additional visual data of one or more images captured at the building, such as to identify at least one image with a known position in a room or other area that has matching visual data (e.g., to determine an inter-image pose between the image's acquisition location and the frame's acquisition location) in order to position the frame within that room or other area (e.g., at an inter-image pose location for the frame); matching one or more building attributes identified in the visual data of the frame to other building attributes determined from the building's floor plan (e.g., to analyze the floor plan to identify structural elements that are matched to visible structural elements in the frame's visual data) in order to position the frame within a room or other area of the floor plan having the matching building attributes (e.g., at a particular position within that room or other area based on the matching); determining that building objects or other building attributes identified from analysis of the frame's visual data are associated with a room type of a room in the building (e.g., associating a stove or refrigerator with a kitchen room type, associating a toilet or shower with a bathroom room type, associating a bed or nightstand with a bedroom room type, etc.), using detected movement patterns for the video's path in the manner discussed above, etc.

In addition, the generation of an additional video from one or more existing videos may be based at least in part on user input in some embodiments and situations about the segment criteria, including in response to information presented to one or more users about the building's floor plan and/or about pieces of media captured at the building, while in other embodiments some or all of the segment criteria may be automatically determined (e.g., one or more rooms or other areas for which to include visual data in the additional video, building attributes for which to include visual data in the additional video, etc.), such as using one or more machine learning models trained to determine such information. As one non-exclusive example, some or all of a floor plan for the building may be presented to a user, optionally with information indicated on the floor plan of acquisition locations of building images and/or of particular building attributes, and optionally with a visual representation of a path of an existing video overlaid on the floor plan—in other embodiments and situations, information about pieces of media captured at the building may be grouped and/or presented in other manners, such as to provide a list or other grouping of one or more media types acquired in part or in whole within each of one or more rooms or other areas (e.g., particular images, videos, audio recordings, etc.). In at least some embodiments, the user may specify some or all of the segment criteria to use in generating an additional video from one or more existing videos, such as by selecting one or more rooms or other areas (e.g., on the presented floor plan) for which to include visual data in the additional video, by selecting a portion of the overlaid visual representation of the existing video's path (e.g., by drawing a box or other shape around that portion) to include in the additional video, by selecting one or more building attributes to include in the visual data of the additional video, etc. In addition, once such an additional video is generated, information about it may similarly be overlaid on or otherwise associated with a floor plan of the building (e.g., to overlay a path of the additional video, to include the additional video on the media pieces shown for one or more rooms or other areas, etc.), and the generated additional video may similarly be presented to one or more users (e.g., to one or more users from which corresponding segment criteria is received). Additional details are included below about analyzing existing videos, identifying various types of information associated with existing videos, generating additional videos from one or more segments of one or more existing videos, presenting videos, presenting information about one or more videos on a floor plan, and presenting other types of building information, including with respect to the examples ofFIGS.2Q-2Sand their associated descriptions.

Automated operations of a BVGUM system may in at least some embodiments include automatically generating one or more new videos for parts of a building using visual data of images acquired at the building and additional information from a floor plan for the building, such as by using one or more specified generation criteria for the new video (e.g., a path with a continuous sequence of locations or other location sequence from which to provide visual data, such as locations on a floor plan of the building; one or more orientations for each such location, such as a direction in three directions from a specified height at that location; a speed at which to move between locations in a sequence; a type of visual transition to use between visual data for non-contiguous or non-adjacent locations in the sequence; etc.)—in at least some embodiments and situations, the generation of such a new video may include using a NeRF (Neural Radiance Field) neural network and associated NeRF processing techniques with images at known locations to generate additional images (e.g., video frames) at other specified positions and orientations, and in at least some embodiments and situations, the generation of such a new video may include using Gaussian Splatting processing techniques with images at known locations to generate additional images (e.g., video frames) at other specified positions and orientations. The additional information from the floor plan that is used in the generation of the new video may include, for example, structural elements and other building attributes of rooms and/or other areas identified from the floor plan and optionally selected to include visual data of them in the video, acquisition locations of the images determined on the floor plan, etc. The existing images may be of various types (e.g., panorama images, such as in equirectangular format; perspective images, such as in rectilinear and/or orthographic format; frames of one or more existing videos, such as a single frame or a sequence of multiple continuous frames; etc.), and the generation of a new video using visual data from one or more existing images may be based at least in part on user input in some embodiments and situations, including in response to information presented to one or more users about the building's floor plan and/or about pieces of media captured at the building, such as to specify some or all of the generation criteria for the new video, while in other embodiments some or all of the generation criteria may be automatically determined (e.g., a path for the new video to follow, building attributes to include in the visual data of the new video, using portions of existing videos to serve as transitions between adjacent rooms, etc.), such as using one or more machine learning models trained to determine such information. As one non-exclusive example, some or all of a floor plan for the building may be presented to a user, optionally with information indicated on the floor plan or otherwise provided about acquisition locations of building images and/or building attributes of the building—in at least some embodiments, the user may specify some or all of the generation criteria from the presented information, such as by selecting one or more rooms or other areas (e.g., on the presented floor plan) for which to include visual data in the new video, by selecting one or more building attributes to include in the visual data of the new video, etc. In addition, once such a new video is generated, information about it may similarly be overlaid on or otherwise associated with a floor plan of the building (e.g., to overlay a path of the new video, to include the new video on the media pieces shown for one or more rooms or other areas, etc.), and the generated new video may similarly be presented to one or more users (e.g., to one or more users from which corresponding segment criteria is received). Additional details are included below about generating new videos using visual data of images, presenting videos, presenting information about one or more videos on a floor plan, and presenting other types of building information, including with respect to the examples ofFIGS.2T-2Vand their associated descriptions.

e In some embodiments and situations, automated operations of a BVGUM system may further include automatically generating and adding additional visual data that is overlaid on one or more generated videos for a building, whether a generated additional video from at least one subset of an existing video acquired at the building, and/or a generated new video using visual data of images acquired at the building and additional information from a floor plan for the building. Such additional overlaid visual data may, for example, include geographic shapes and/or outlines and/or other visual representations of objects at the building (e.g., objects that are partially or completely blocked or otherwise occluded from a current position and orientation of the visual data of the generated video, such as objects in a same room as the current position but blocked by one or more other objects and/or structural building elements, objects in a different room or other building area, such as an external area, from that of the current position that are blocked by one or more walls and/or other objects, etc.), while in other embodiments and situations some or all of the additional overlaid visual data may include visual data of other types (e.g., visual representations of virtual objects that are not physically present at the building). The additional visual data may be generated and included in a generated video at a time of the generation of that video in some embodiments and situations, and may be generated and included in a generated video after the generation of that video in some embodiments and situations.

Automated operations of a BVGUM system may in at least some embodiments include automatically analyzing visual data of images acquired in and around a building, and optionally associated image acquisition metadata (e.g., orientation information for an image, such as using heading information from a compass sensor, location information from a GPS sensor, etc.), to generate one or more videos that describe the building. The automated techniques may further include selecting one or more groups of images for a building, and for each such image group, generating a video that includes visual coverage corresponding to selected building attributes of interest and that further includes audible narration based on automatically generated textual descriptions about the building attributes and optionally based on additional information (e.g., about the building as a whole, about transitions between multiple images of a group, etc.). In at least some such embodiments, the automated operations include selecting one or more building images to use in generating a video for the building, including to determine a sequence of the images if multiple images are selected—such image selection may include, for example, selecting images corresponding to particular rooms or other areas, that highlight particular types of building attributes, that have particular types of characteristics, etc. Given a group of one or more selected images, a visual portion of a resulting video may be based on various types of manipulations of the visual data of such images, with non-exclusive examples including zooming, panning (e.g., within a panorama image), tilting, etc., including to highlight or emphasize particular attributes of the building that are of interest to describe, as well as using various types of transitions between the visual data of different images in a sequence. Narration to accompany the video may be further automatically generated and synchronized with the video, including to provide narrative descriptions of selected building attributes, as discussed further below. In at least some such embodiments, one or more machine learning models (e.g., one or more neural networks) may be used by the BVGUM system to perform such image selection and sequence determination, and may be trained via supervised learning (e.g., using labeled versions of user-generated videos, such as video house tours generated by professional photographers or videographers), while in other embodiments such machine learning models may instead be trained in an unsupervised manner (e.g., using unsupervised clustering). With respect to the building images that are used in video generation, some or all of the images acquired for a building and used in video generation may in at least some embodiments and situations be panorama images that are each acquired at one of multiple acquisition locations in or around the building, such as to generate a panorama image at each such acquisition location from one or more of a video at that acquisition location (e.g., a 360° video taken from a smartphone or other mobile device held by a user turning at that acquisition location), or multiple images acquired in multiple directions from the acquisition location (e.g., from a smartphone or other mobile device held by a user turning at that acquisition location), or a simultaneous capture of all the image information (e.g., using one or more fisheye lenses), etc. It will be appreciated that such a panorama image may in some situations be represented in a spherical coordinate system and provide up to 360° coverage around horizontal and/or vertical axes, such that a user viewing a starting panorama image may move the viewing direction within the starting panorama image to different orientations to cause different images (or “views”) to be rendered within the starting panorama image (including, if the panorama image is represented in a spherical coordinate system, to convert the image being rendered into a planar coordinate system). Furthermore, acquisition metadata regarding the capture of such panorama images may be obtained and used in various manners, such as data acquired from IMU (inertial measurement unit) sensors or other sensors of a mobile device as it is carried by a user or otherwise moved between acquisition locations. Additional details are included below regarding automated generation of building videos from building images, including with respect to the examples ofFIGS.2D-2Pand their associated descriptions.

As noted above, automated operations of a BVGUM system may in at least some embodiments include automatically determining attributes of interest for a building based at least in part on analyzing visual data of images acquired in and around a building and optionally associated image acquisition metadata, including in at least some situations by using one or more trained machine learning models (whether the same or different machine learning models used to select images to use in video generation and/or to determine segment criteria and/or to determine generation criteria and/or to perform the video generation)—in other embodiments, information about some or all of the building attributes may instead be determined in other manners, such as in part from an existing textual building description. Such determined attributes may reflect characteristics of individual rooms or other areas of the building, such as corresponding to structural elements and other objects identified in the rooms and/or visible characteristics or other attributes of the objects and the rooms—in particular, the automated analysis by the BVGUM system of building images may, in at least some embodiments and situations, include identifying structural elements or other objects of various types in rooms of the building or otherwise in areas associated with the building (e.g., external areas, additional accessory buildings or other structures, etc.), with non-exclusive examples of such objects including a floor, wall, ceiling, window, doorway, non-doorway wall opening, set of stairs, fixture (e.g., lighting or plumbing), appliance, cabinet, island, fireplace, countertop, other built-in structural element, furniture, etc. The automated analysis by the BVGUM system of acquired building images may further include determining particular attributes of each of some or all such identified objects, such as, for example, a color, type of material (e.g., surface material), estimated age, etc., as well as additional types of attributes in some embodiments such as directions that building objects face (e.g., for windows, doorways, etc.), natural lighting at particular positions (e.g., based on the geographical location and orientation of the building and the position of the sun at a specified time, such as a time-of-day, day-of-month, month-of-year, season-of-year, etc., and optionally corresponding to a particular object), views from particular windows or other locations, etc. Attributes determined for a particular room from one or more images acquired in the room (or otherwise from one or more images acquired at positions with a view of at least some of the room) may include, for example, one or more of the following non-exclusive examples: room types, room dimensions, room shape (e.g., two-dimensional, or ‘2D’, such as relative positions of walls; three-dimensional, or ‘3D’, such as a 3D point cloud and/or planar surfaces of walls and a floor and a ceiling; etc.), types of room usage (e.g., public versus private space) and/or functionality (e.g., recreation), locations in a room of windows and doorways and other inter-room openings, types of inter-room connections, dimensions of inter-room connections, etc. In at least some such embodiments, the BVGUM system may, for such automated analysis of images, use one or more machine learning models (e.g., classification neural network models) that are trained via supervised learning (e.g., using labeled data that identifies images having each of the possible objects and attributes), while in other embodiments such machine learning models may instead be trained in an unsupervised manner (e.g., using unsupervised clustering). Additional details are included below regarding automated analysis of acquired images and/or other environmental data associated with a building to determine attributes of the building and of its rooms, including with respect to the examples ofFIGS.2D-2Pand their associated descriptions.

As noted above, automated operations of a BVGUM system may also in at least some embodiments include automatically analyzing types of building information other than acquired building images to determine additional attributes of the building, including in at least some situations by using one or more trained machine learning models (e.g., one or more trained neural networks, and whether the same or different from the machine learning models used to analyze images and/or to select images for videos and/or to generate videos from selected images and/or to determine segment criteria and/or to determine generation criteria) to determine attributes that reflect characteristics of some or all of the building (e.g., of two or more rooms of the building), such as corresponding to some or all of a layout of some or all rooms of the building (e.g., based at least in part on inter-connections between rooms and/or other inter-room adjacencies)—such other types of building information may include, for example, one or more of the following: a floor plan; a group of inter-linked images, such as for use in a virtual tour; an existing textual description of a building (e.g., listing information for a building, such as is included on a Multiple Listing Service, or MLS); etc. Such a floor plan of a building may include a 2D (two-dimensional) representation of various information about the building (e.g., the rooms, doorways between rooms and other inter-room connections, exterior doorways, windows, etc.), and may be further associated with various types of supplemental or otherwise additional information about the building (e.g., data for a plurality of other building-related attributes)—such additional building information may, for example, include one or more of the following: a 3D, or three-dimensional, model of the building that includes height information (e.g., for building walls and inter-room openings and other vertical areas); a 2.5D, or two-and-a-half dimensional, model of the building that when rendered includes visual representations of walls and/or other vertical surfaces without explicitly modeling measured heights of those walls and/or other vertical surfaces; images and/or other types of data captured in rooms of the building, including panoramic images (e.g., 360° panorama images); etc., as discussed in greater detail below. In some embodiments and situations, the floor plan and/or its associated information may further represent at least some information external to the building (e.g., for some or all of a property on which the building is located), such as exterior areas adjacent to doorways or other wall openings between the building and the exterior, or more generally some or all external areas of a property that includes one or more buildings or other structures (e.g., a house and one or more outbuildings or other accessory structures, such as a garage, shed, pool house, separate guest quarters, mother-in-law unit or other accessory dwelling unit, pool, patio, deck, sidewalk, etc.).

The automated analysis by the BVGUM system of a building floor plan and/or other building information may, in at least some embodiments and situations, include determining building attributes that are based on information about a building as a whole, such as objective attributes that can be independently verified and/or replicated (e.g., number of bedrooms, number of bathrooms, square footage, connectivity between rooms, etc.), and/or subjective attributes that have associated uncertainty (e.g., whether the building has an open floor plan; has a typical/normal layout versus atypical/odd/unusual layout; a standard versus nonstandard floor plan; a floor plan that is accessibility friendly, such as by being accessible with respect to one or more characteristics such as wheelchair or other disability and/or advanced age; etc.). The automated analysis by the BVGUM system of a building floor plan may, in at least some embodiments and situations, further include determining building attributes that are based at least in part on information about inter-room adjacencies (e.g., inter-room connections between two or more rooms or other areas), such as based at least in part on a layout of some or all rooms of a building (e.g., all rooms on the same story or that are otherwise part of a grouping of rooms), including some or all such subjective attributes, as well as other types of attributes such as a movement flow pattern of people through rooms. At least some such determined building attributes may be further based on information about a building's location and/or orientation (e.g., about views available from windows or other exterior openings of the building, about directions of windows or other structural elements or other objects of the building, about natural lighting information available at specified days and/or seasons and/or times, etc.). In at least some such embodiments, the BVGUM system may, for such automated analysis of building floor plans, use one or more machine learning models (e.g., classification neural network models) that are trained via supervised learning (e.g., using labeled data that identifies floor plans or other groups of rooms or other areas having each of the possible characteristics or other attributes), while in other embodiments such machine learning models may instead be trained in an unsupervised manner (e.g., using unsupervised clustering). Additional details are included below regarding automated analysis of a floor plan for a building to determine attributes of the building, including with respect to the examples ofFIGS.2D-2Vand their associated descriptions.

As noted above, automated operations of a BVGUM system may also in at least some embodiments include automated generation of descriptions about a building based on automatically determined characteristics and other attributes, including, in at least some embodiments and situations, using one or more trained language models to generate a description for each of some or all such determined attributes. The generated descriptions for individual attributes may be further combined in various manners in various embodiments, such as by grouping attributes and their associated descriptions in various manners (e.g., by room or other area; by type of attribute, such as by object type and/or color and/or surface material; by degree of specificity or generality, such as to group building-wide attributes and include their generated descriptions, followed by generated descriptions for attributes that are grouped by room, followed by generated descriptions for attributes that correspond to individual structural elements and other objects; etc.). After attributes and/or building descriptions are generated or otherwise obtained for a building, such as based on analysis of information for a building (e.g., images of, a floor plan for, and optionally other associated information for a building), that generated building information may be used by the BVGUM system in various manners, including in some embodiments as part of generating a narration that accompanies the visual data of a generated video and describes information shown in the visual data. Such generation of a video narration may include, for example, using one or more trained language models that take input such as objects and/or other attributes, associated location information (e.g., one or more rooms, one or more stories or other groups of rooms, etc.), timing and/or sequence information (e.g., a series of objects and/or other attributes to be highlighted or otherwise shown in a video), etc., and generate corresponding textual descriptions. Additional details are included below regarding automatically generating descriptions of determined building attributes and of using such generated descriptions as part of video narrations, including with respect to the examples ofFIGS.2D-2Pand their associated descriptions.

After videos are automatically generated for a building based on analysis of images and optionally other associated information of a building, that generated building information may also be used by the BVGUM system in some embodiments to automatically determine that the building matches one or more specified criteria (e.g., search criteria) in various manners in various embodiments, including to identify that a building is similar to or otherwise matches one or more other buildings based on their corresponding videos or other building information. Such criteria may include any one or more attributes or specified combinations of them, and/or more generally may match content of a building video narration, with examples including based on particular objects and/or other attributes, based on adjacency information about which rooms are inter-connected and related inter-room relationship information (e.g., with respect to overall building layout), based on particular rooms or other areas and/or to attributes of those rooms or other areas, etc. Non-exclusive and non-limiting illustrative examples of criteria may include a kitchen with a tile-covered island and dark-colored wood floor and a northward-facing view; a building having a bathroom adjacent to bedroom (i.e., without an intervening hall or other room); a deck adjacent to a family room (optionally with a specified type of connection between them, such as French doors);2bedrooms facing south; a master bedroom on a second story with a view of the ocean or more generally of water; any combination of such specified criteria; etc. Additional details are included below regarding using generated information for a building to assist in further identification of the building as matching specified criteria or otherwise being of use, including with respect to the examples ofFIGS.2D-2Vand their associated description.

The described techniques provide various benefits in various embodiments, including to allow information about multi-room buildings and other structures to be identified and used more efficiently and rapidly and in manners not previously available, including to generate one or more videos for a building that have at least visual data for selected or automatically determined types of building data, such as to provide improved navigation of the building and/or to assist in identifying other related buildings. In addition, the described techniques may assist in automatically identifying buildings that match specified criteria based at least in part on automated analysis of various types of building information (e.g., images, floor plans, etc.)—such criteria may be based, for example, on one or more of the following: attributes of particular objects within the building (e.g., in particular rooms or other areas, or more generally attributes of those rooms or other areas), such as determined from analysis of one or more images acquired at the building; similarity to one or more other buildings; adjacency information about which rooms are inter-connected and related inter-room relationship information, such as with respect to overall building layout; similarity to particular building or other area characteristics or other attributes; similarity to subjective attributes regarding a floor plan's characteristics, etc. In addition, such automated techniques allow such identification of matching buildings to be determined by using information acquired from the actual building environment (rather than from plans on how the building should theoretically be constructed), as well as enabling the capture of changes to structural elements and/or visual appearance elements that occur after a building is initially constructed. Such described techniques further provide benefits in allowing improved automated navigation of a building by mobile devices (e.g., semi-autonomous or fully autonomous vehicles), based at least in part on the identification of buildings that match specified criteria, including to significantly reduce computing power and time used to attempt to otherwise learn a building's layout. In addition, in some embodiments the described techniques may be used to provide an improved GUI in which a user may more accurately and quickly identify one or more buildings matching specified criteria, and obtain information about indicated buildings (e.g., for use in navigating an interior of the one or more buildings), including in response to search requests, as part of providing personalized information to the user, as part of providing value estimates and/or other information about a building to a user (e.g., after analysis of information about one or more target building floor plans that are similar to one or more initial floor plans or that otherwise match specified criteria), etc. Various other benefits are also provided by the described techniques, some of which are further described elsewhere herein.

In addition, in some embodiments, one or more target buildings are identified that are similar to specified criteria associated with a particular end-user (e.g., based on one or more initial buildings that are selected by the end-user and/or are identified as previously being of interest to the end-user, whether based on explicit and/or implicit activities of the end-user to specify such buildings; based on one or more search criteria specified by the end-user, whether explicitly and/or implicitly; etc.), and are used in further automated activities to personalize interactions with the end-user. Such further automated personalized interactions may be of various types in various embodiments, and in some embodiments may include displaying or otherwise presenting information to the end-user about the target building(s) and/or additional information associated with those buildings. Furthermore, in at least some embodiments, the videos that are generated or otherwise presented to an end-user may be personalized to that end-user in various manners, such as based on a video length, types of rooms shown, types of attributes shown, etc., including in some embodiments to dynamically generate a new building video for an end-user recipient based on information specific to that recipient, to select one of multiple available videos for a building to present to an end-user recipient based on information specific to that recipient, to customize an existing building video for an end-user recipient end-based on information specific to that recipient (e.g., to remove portions of the existing video), etc. Additional details are included below regarding end-user personalization and/or presentation with respect to indicated buildings, including with respect to the examples ofFIGS.2D-2Vand their associated descriptions.

As noted above, automated operations of a BVGUM system may include using acquired building images and/or other building information, such as a floor plan. In at least some embodiments, such an BVGUM system may operate in conjunction with one or more separate ICA (Image Capture and Analysis) systems and/or with one or more separate MIGM (Mapping Information and Generation Manager) systems, such as to obtain and use images and floor plans and other associated information for buildings from the ICA and/or MIGM systems, while in other embodiments such an BVGUM system may incorporate some or all functionality of such ICA and/or MIGM systems as part of the BVGUM system. In yet other embodiments, the BVGUM system may operate without using some or all functionality of the ICA and/or MIGM systems, such as if the BVGUM system obtains building images, floor plans and/or other associated information from other sources (e.g., from manual creation or provision of such building images, floor plans and/or associated information by one or more users).

With respect to functionality of such an ICA system, it may perform automated operations in at least some embodiments to acquire images (e.g., panorama images) at various acquisition locations associated with a building (e.g., in the interior of multiple rooms of the building), and optionally further acquire metadata related to the image acquisition process (e.g., image pose information, such as using compass headings and/or GPS-based locations) and/or to movement of a capture device between acquisition locations—in at least some embodiments, such acquisition and subsequent use of acquired information may occur without having or using information from depth sensors or other distance-measuring devices about distances from images' acquisition locations to walls or other objects in a surrounding building or other structure. For example, in at least some such embodiments, such techniques may include using one or more mobile devices (e.g., a camera having one or more fisheye lenses and mounted on a rotatable tripod or otherwise having an automated rotation mechanism; a camera having one or more fisheye lenses sufficient to capture 360° horizontally without rotation; a smart phone held and moved by a user, such as to rotate the user's body and held smart phone in a 360° circle around a vertical axis; a camera held by or mounted on a user or the users clothing; a camera mounted on an aerial and/or ground-based drone or other robotic device; etc.) to capture visual data from a sequence of multiple acquisition locations within multiple rooms of a house (or other building). Additional details are included elsewhere herein regarding operations of device(s) implementing an ICA system, such as to perform such automated operations, and in some cases to further interact with one or more ICA system operator user(s) in one or more manners to provide further functionality.

With respect to functionality of such an MIGM system, it may perform automated operations in at least some embodiments to analyze multiple 360° panorama images (and optionally other images) that have been acquired for a building interior (and optionally an exterior of the building), and generate a corresponding floor plan for the building, such as by determining room shapes and locations of passages connecting rooms for some or all of those panorama images, as well as by determining structural wall elements and optionally other objects in some or all rooms of the building in at least some embodiments and situations. The types of structural wall elements corresponding to connecting passages between two or more rooms may include one or more of doorway openings and other inter-room non-doorway wall openings, windows, stairways, non-room hallways, etc., and the automated analysis of the images may identify such elements based at least in part on identifying the outlines of the passages, identifying different content within the passages than outside them (e.g., different colors or shading), etc. The automated operations may further include using the determined information to generate a floor plan for the building and to optionally generate other mapping information for the building, such as by using the inter-room passage information and other information to determine relative positions of the associated room shapes to each other, and to optionally add distance scaling information and/or various other types of information to the generated floor plan. In addition, the MIGM system may in at least some embodiments perform further automated operations to determine and associate additional information with a building floor plan and/or specific rooms or locations within the floor plan, such as to analyze images and/or other environmental information (e.g., audio) captured within the building interior to determine particular objects and attributes (e.g., a color and/or material type and/or other characteristics of particular structural elements or other objects, such as a floor, wall, ceiling, countertop, furniture, fixture, appliance, cabinet, island, fireplace, etc.; the presence and/or absence of particular objects or other elements; etc.), or to otherwise determine relevant attributes (e.g., directions that building objects face, such as windows; views from particular windows or other locations: etc.). Additional details are included below regarding operations of computing device(s) implementing an MIGM system, such as to perform such automated operations and in some cases to further interact with one or more MIGM system operator user(s) in one or more manners to provide further functionality.

For illustrative purposes, some embodiments are described below in which specific types of information are acquired, used and/or presented in specific ways for specific types of structures and by using specific types of devices—however, it will be understood that the described techniques may be used in other manners in other embodiments, and that the invention is thus not limited to the exemplary details provided. As one non-exclusive example, while specific types of data structures (e.g., videos, floor plans, virtual tours of inter-linked images, generated building descriptions, etc.) are generated and used in specific manners in some embodiments, it will be appreciated that other types of information to describe buildings may be similarly generated and used in other embodiments, including for buildings (or other structures or layouts) separate from houses, and that buildings identified as matching specified criteria may be used in other manners in other embodiments. In addition, the term “building” refers herein to any partially or fully enclosed structure, typically but not necessarily encompassing one or more rooms that visually or otherwise divide the interior space of the structure—non-limiting examples of such buildings include houses, apartment buildings or individual apartments therein, condominiums, office buildings, commercial buildings or other wholesale and retail structures (e.g., shopping malls, department stores, warehouses, etc.), supplemental structures on a property with another main building (e.g., a detached garage or shed on a property with a house), etc. The term “acquire” or “capture” as used herein with reference to a building interior, acquisition location, or other location (unless context clearly indicates otherwise) may refer to any recording, storage, or logging of media, sensor data, and/or other information related to spatial characteristics and/or visual characteristics and/or otherwise perceivable characteristics of the building interior or subsets thereof, such as by a recording device or by another device that receives information from the recording device. As used herein, the term “panorama image” may refer to a visual representation that is based on, includes or is separable into multiple discrete component images originating from a substantially similar physical location in different directions and that depicts a larger field of view than any of the discrete component images depict individually, including images with a sufficiently wide-angle view from a physical location to include angles beyond that perceivable from a person's gaze in a single direction. The term “sequence” of acquisition locations, as used herein, refers generally to two or more acquisition locations that are each visited at least once in a corresponding order, whether or not other non-acquisition locations are visited between them, and whether or not the visits to the acquisition locations occur during a single continuous period of time or at multiple different times, or by a single user and/or device or by multiple different users and/or devices. In addition, various details are provided in the drawings and text for exemplary purposes, but are not intended to limit the scope of the invention. For example, sizes and relative positions of elements in the drawings are not necessarily drawn to scale, with some details omitted and/or provided with greater prominence (e.g., via size and positioning) to enhance legibility and/or clarity. Furthermore, identical reference numbers may be used in the drawings to identify the same or similar elements or acts.

FIG.1Aincludes an example block diagram of various computing devices and systems that may participate in the described techniques in some embodiments, such as with respect to the illustrated example building198(in this example, a house) and the example Building Video Generation and Usage Manager (“BVGUM”) system140executing on one or more server computing systems180in this example embodiment. In the illustrated embodiment, the BVGUM system140analyzes obtained building information142(e.g., images, such as images165acquired by the ICA system; floor plans, such as floor plans155generated by the MIGM system; existing building videos; etc.) for one or more buildings and uses that building information and information generated from its analysis to generate one or more building videos141for the one or more buildings with visual data for the one or more buildings and that in some situations include accompanying narration, optionally using supporting information supplied by system operator users via computing devices105over intervening computer network(s)170, and in some embodiments and situations by using one or more trained machine learning and/or language models144as part of the analysis of the building information142and/or generation of the videos141—in some embodiments, the building information142that is analyzed by the BVGUM system may be obtained in manners other than via ICA and/or MIGM systems (e.g., if such ICA and/or MIGM systems are not part of the BVGUM system), such as to receive building images and/or floor plans and/or existing building videos from other sources. The BVGUM system may further use such generated building information in one or more further automated manners, including in some embodiments as part of identifying buildings that match each other or other indicated criteria—such criteria may in some embodiments and situations be supplied by or otherwise associated with particular users (e.g., objects or other attributes specified by users, floor plans or other building information indicated by those users, other existing building videos previously identified as being of interest to the users, etc.), and corresponding information143about various users may further optionally be stored and used in the identifying of the buildings that satisfy such criteria and the subsequent use of the identified buildings' information (e.g., use of the buildings' generated videos) in one or more further automated manners. In addition, in at least some embodiments and situations, one or more users of client computing devices105may further interact over the network(s)170with the BVGUM system140, such as to assist with some of the automated operations of the BVGUM system for identifying buildings satisfying criteria and/or subsequent use of the identified floor plans in one or more further automated manners. Additional details related to the automated operations of the BVGUM system are included elsewhere herein, including with respect toFIGS.2D-2VandFIGS.4A-4D.

In addition, in this example, an Interior Capture and Analysis (“ICA”) system (e.g., an ICA system160executing on the one or more server computing systems180, such as part of the BVGUM system; an ICA system application154executing on a mobile image acquisition device185; etc.) captures information165with respect to one or more buildings or other structures (e.g., by capturing one or more 360° panorama images and/or other images for multiple acquisition locations210in example house198), and a MIGM (Mapping Information Generation Manager) system160executing on the one or more server computing systems180(e.g., as part of the BVGUM system) further uses that captured building information and optionally additional supporting information (e.g., supplied by system operator users via computing devices105over intervening computer network(s)170) to generate and provide building floor plans155and/or other mapping-related information (not shown) for the building(s) or other structure(s). While the ICA and MIGM systems160are illustrated in this example embodiment as executing on the same server computing system(s)180as the BVGUM system (e.g., with all systems being operated by a single entity or otherwise being executed in coordination with each other, such as with some or all functionality of all the systems integrated together), in other embodiments the ICA system160and/or MIGM system160and/or BVGUM system140may operate on one or more other systems separate from the system(s)180(e.g., on mobile device185; one or more other computing systems, not shown; etc.), whether instead of or in addition to the copies of those systems executing on the system(s)180(e.g., to have a copy of the MIGM system160executing on the device185to incrementally generate at least partial building floor plans as building images are acquired by the ICA system160executing on the device185and/or by that copy of the MIGM system, while another copy of the MIGM system optionally executes on one or more server computing systems to generate a final complete building floor plan after all images are acquired), and in yet other embodiments the BVGUM may instead operate without an ICA system and/or MIGM system and instead obtain panorama images (or other images) and/or building floor plans from one or more external sources. Additional details related to the automated operation of the ICA and MIGM systems are included elsewhere herein, including with respect toFIGS.2A-2Dand with respect toFIGS.5and6A-6B, respectively.

Various components of the mobile image acquisition computing device185are also illustrated inFIG.1A, including one or more hardware processors132(e.g., CPUs, GPUs, etc.) that execute software (e.g., ICA application154, optional browser162, etc.) using executable instructions stored and/or loaded on one or more memory/storage components152of the device185, and optionally one or more imaging systems135of one or more types to acquire visual data of one or more panorama images165and/or other images (not shown, such as rectilinear perspective images)—some or all such images165may in some embodiments be supplied by one or more separate associated camera devices184(e.g., via a wired/cabled connection, via Bluetooth or other inter-device wireless communications, etc.), whether in addition to or instead of images captured by the mobile device185. The illustrated embodiment of mobile device185further includes one or more sensor modules148that include a gyroscope148a, accelerometer148band compass148cin this example (e.g., as part of one or more IMU units, not shown separately, on the mobile device), one or more control systems147managing I/O (input/output) and/or communications and/or networking for the device185(e.g., to receive instructions from and present information to the user) such as for other device I/O and communication components143(e.g., network interfaces or other connections, keyboards, mice or other pointing devices, microphones, speakers, GPS receivers, etc.), a display system149(e.g., with a touch-sensitive screen), optionally one or more depth-sensing sensors or other distance-measuring components136of one or more types, optionally a GPS (or Global Positioning System) sensor134or other position determination sensor (not shown in this example), optionally other components (e.g., one or more lighting components), etc. Other computing devices/systems105,175and180and/or camera devices184may include various hardware components and stored information in a manner analogous to mobile device185, which are not shown in this example for the sake of brevity, and as discussed in greater detail below with respect toFIG.3.

One or more users (e.g., end users, not shown) of one or more client computing devices175may further interact over one or more computer networks170with the BVGUM system140(and optionally the ICA system160and/or MIGM system160), such as to participate in providing input to use in generating videos and/or to receive presented information about generated videos and corresponding buildings and/or identifying buildings satisfying target criteria and/or identifying building videos satisfying target criteria, as well as subsequently using identified and/or generated information (e.g., generated building videos) in one or more further automated manners—such client computing devices may each execute a building information access system (not shown) that is used by the users in the interactions, as discussed in greater detail elsewhere herein, including with respect toFIGS.7A-7B. Such interactions by the user(s) may include, for example, specifying criteria to use in generating videos, or specifying criteria to use in searching for corresponding buildings or building videos or otherwise providing building information about criteria of interest to the users, or obtaining and optionally requesting information for one or more indicated buildings (e.g., to play or otherwise present a sequence of multiple videos for one or more buildings, such as in a playlist and/or by using auto-play functionality) and interacting with corresponding provided building information (e.g., to change between a floor plan view and a view of a particular image at an acquisition location within or near the floor plan; to change the horizontal and/or vertical viewing direction from which a corresponding view of a panorama image is displayed, such as to determine a portion of a panorama image to which a current user viewing direction is directed; to view generated textual building information or other generated building information; etc.). In addition, a floor plan (or portion of it) may be linked to or otherwise associated with one or more other types of information, including for a floor plan of a multi-story or otherwise multi-level building to have multiple associated sub-floor plans for different stories or levels that are interlinked (e.g., via connecting stairway passages), for a two-dimensional (“2D”) floor plan of a building to be linked to or otherwise associated with a three-dimensional (“3D”) rendering of the building, etc. Also, while not illustrated inFIG.1A, in some embodiments the client computing devices175(or other devices, not shown) may receive and use information about buildings (e.g., identified floor plans and/or other mapping-related information) in additional manners, such as to control or assist automated navigation activities by those devices (e.g., by autonomous vehicles or other devices), whether instead of or in addition to display of the identified information.

In the depicted computing environment ofFIG.1A, the network170may be one or more publicly accessible linked networks, possibly operated by various distinct parties, such as the Internet. In other implementations, the network170may have other forms. For example, the network170may instead be a private network, such as a corporate or university network that is wholly or partially inaccessible to non-privileged users. In still other implementations, the network170may include both private and public networks, with one or more of the private networks having access to and/or from one or more of the public networks. Furthermore, the network170may include various types of wired and/or wireless networks in various situations. In addition, the client computing devices175and server computing systems180may include various hardware components and stored information, as discussed in greater detail below with respect toFIG.3.

In the example ofFIG.1A, the ICA system may perform automated operations involved in generating multiple 360° panorama images at multiple associated acquisition locations (e.g., in multiple rooms or other locations within a building or other structure and optionally around some or all of the exterior of the building or other structure), such as using visual data acquired via the mobile device(s)185and/or associated camera devices184, and for use in generating and providing a representation of an interior of the building or other structure. For example, in at least some such embodiments, such techniques may include using one or more mobile devices (e.g., a camera having one or more fisheye lenses and mounted on a rotatable tripod or otherwise having an automated rotation mechanism, a camera having sufficient fisheye lenses to capture 360° horizontally without rotation, a smart phone held and moved by a user, a camera held by or mounted on a user or the user's clothing, etc.) to capture data from a sequence of multiple acquisition locations within multiple rooms of a house (or other building), and to optionally further capture data involved in movement of the acquisition device (e.g., movement at an acquisition location, such as rotation; movement between some or all of the acquisition locations, such as for use in linking the multiple acquisition locations together; etc.), in at least some cases without having distances between the acquisition locations being measured or having other measured depth information to objects in an environment around the acquisition locations (e.g., without using any depth-sensing sensors). After an acquisition location's information is captured, the techniques may include producing a 360° panorama image from that acquisition location with 360° of horizontal information around a vertical axis (e.g., a 360° panorama image that shows the surrounding room in an equirectangular format), and then providing the panorama images for subsequent use by the MIGM and/or BVGUM systems. Additional details related to embodiments of a system providing at least some such functionality of an ICA system are included in U.S. Non-Provisional patent application Ser. No. 16/693,286, filed Nov. 23, 2019 and entitled “Connecting And Using Building Data Acquired From Mobile Devices” (which includes disclosure of an example BICA system that is generally directed to obtaining and using panorama images from within one or more buildings or other structures); in U.S. Non-Provisional patent application Ser. No. 16/236,187, filed Dec. 28, 2018 and entitled “Automated Control Of Image Acquisition Via Use Of Acquisition Device Sensors” (which includes disclosure of an example ICA system that is generally directed to obtaining and using panorama images from within one or more buildings or other structures); and in U.S. Non-Provisional patent application Ser. No. 16/190,162, filed Nov. 14, 2018 and entitled “Automated Mapping Information Generation From Inter-Connected Images”; each of which is incorporated herein by reference in its entirety.

In addition, while not illustrated inFIG.1A, a floor plan (or portion of it) may be linked to or otherwise associated with one or more additional types of information, such as one or more associated and linked images or other associated and linked information, including for a two-dimensional (“2D”) floor plan of a building to be linked to or otherwise associated with a separate 2.5D model floor plan rendering of the building and/or a 3D model floor plan rendering of the building, etc., and including for a floor plan of a multi-story or otherwise multi-level building to have multiple associated sub-floor plans for different stories or levels that are interlinked (e.g., via connecting stairway passages) or are part of a common 2.5D and/or 3D model. Accordingly, non-exclusive examples of an end user's interactions with a displayed or otherwise generated 2D floor plan of a building may include one or more of the following: to change between a floor plan view and a view of a particular image at an acquisition location within or near the floor plan; to change between a 2D floor plan view and a 2.5D or 3D model view that optionally includes images texture-mapped to walls of the displayed model; to change the horizontal and/or vertical viewing direction from which a corresponding subset view of (or portal into) a panorama image is displayed, such as to determine a portion of a panorama image in a 3D coordinate system to which a current user viewing direction is directed, and to render a corresponding planar image that illustrates that portion of the panorama image without the curvature or other distortions present in the original panorama image; etc. Additional details regarding a BIIP (Building Information Integrated Presentation) system and/or an ILTM (Image Locations Transition Manager) system, which are example embodiments of systems to provide or otherwise support at least some functionality of a building information access system and routine as discussed herein, are included in U.S. Non-Provisional patent application Ser. No. 16/681,787, filed Nov. 12, 2019 and entitled “Presenting Integrated Building Information Using Three-Dimensional Building Models,” and in U.S. Non-Provisional patent application Ser. No. 15/950,881, filed Apr. 11, 2018 and entitled “Presenting Image Transition Sequences Between Acquisition Locations,” each of which is incorporated herein by reference in its entirety. In addition, while not illustrated inFIG.1A, in some embodiments the client computing devices175(or other devices, not shown) may receive and use generated floor plans and/or other generated mapping-related information in additional manners, such as to control or assist automated navigation activities by those devices (e.g., by autonomous vehicles or other devices), whether instead of or in addition to display of the generated information.

FIG.1Afurther depicts an exemplary building interior environment in which 360° panorama images and/or other images are acquired, such as by the ICA system and for use by the MIGM system (e.g., under control of the BVGUM system) to generate and provide one or more corresponding building floor plans (e.g., multiple incremental partial building floor plans) and/or by the BVGUM system to further use such building information as part of automated building information generation operations. In particular,FIG.1Aillustrates one story of a multi-story house (or other building)198with an interior that was captured at least in part via multiple panorama images, such as by a mobile image acquisition device185with image acquisition capabilities and/or one or more associated camera devices184as they are moved through the building interior to a sequence of multiple acquisition locations210(e.g., starting at acquisition location210A, moving to acquisition location210B along travel path115, etc., and ending at acquisition location210-O or210P outside of the building). An embodiment of the ICA system may automatically perform or assist in the capturing of the data representing the building interior (as well as to further analyze the captured data to generate 360° panorama images to provide a visual representation of the building interior), and an embodiment of the MIGM system may analyze the visual data of the acquired images to generate one or more building floor plans for the house198(e.g., multiple incremental building floor plans). While such a mobile image acquisition device may include various hardware components, such as a camera, one or more sensors (e.g., a gyroscope, an accelerometer, a compass, etc., such as part of one or more IMUs, or inertial measurement units, of the mobile device; an altimeter; light detector, etc.), a GPS receiver, one or more hardware processors, memory, a display, a microphone, etc., the mobile device may not in at least some embodiments have access to or use equipment to measure the depth of objects in the building relative to a location of the mobile device, such that relationships between different panorama images and their acquisition locations in such embodiments may be determined in part or in whole based on elements in different images but without using any data from any such depth sensors, while in other embodiments such depth data may be used. In addition, while directional indicator109is provided inFIG.1Afor reference of the reader relative to the example house198, the mobile device and/or ICA system may not use such absolute directional information and/or absolute locations in at least some embodiments, such as to instead determine relative directions and distances between acquisition locations210without regard to actual geographical positions or directions in such embodiments, while in other embodiments such absolute directional information and/or absolute locations may be obtained and used.

In operation, the mobile device185and/or camera device(s)184arrive at a first acquisition location210A within a first room of the building interior (in this example, in a living room accessible via an external door190-1), and captures or acquires a view of a portion of the building interior that is visible from that acquisition location210A (e.g., some or all of the first room, and optionally small portions of one or more other adjacent or nearby rooms, such as through doorway wall openings, non-doorway wall openings, hallways, stairways or other connecting passages from the first room). The view capture may be performed in various manners as discussed herein, and may include a number of structural elements or other objects that may be visible in images captured from the acquisition location—in the example ofFIG.1A, such objects within the building198include the walls, floors, ceilings, doorways190(including190-1through190-6, such as with swinging and/or sliding doors), windows196(including196-1through196-8), borders between walls and other walls/ceilings/floors such as for inter-wall corners or edges195(including corner195-1in the northwest corner of the building198, corner195-2in the northeast corner of the first room, corner195-3in the southwest corner of the first room, corner195-4in the southeast corner of the first room, corner195-5at the northern edge of the inter-room passage between the first room and a hallway, etc.), furniture191-193(e.g., a couch191; chair192; table193; etc.), pictures or paintings or televisions or other hanging objects194(such as194-1and194-2) hung on walls, light fixtures (not shown inFIG.1A), various built-in appliances or other fixtures or other structural elements (not shown inFIG.1A), etc. The user may also optionally provide a textual or auditory label identifier to be associated with an acquisition location and/or a surrounding room, such as “living room” for one of acquisition locations210A or210B or for the room including acquisition locations210A and/or210B, and/or a descriptive annotation with one or more phrases or sentences about a room and/or one or more objects in the room, while in other embodiments the ICA and/or MIGM system may automatically generate such identifiers and/or annotations (e.g., by automatically analyzing images and/or video and/or other recorded information for a building to perform a corresponding automated determination, such as by using machine learning; based at least in part on input from ICA and/or MIGM system operator users; etc.) or the identifiers may not be used.

After the first acquisition location210A has been captured, the mobile device185and/or camera device(s)184may be moved or move under their own power to a next acquisition location (such as acquisition location210B), optionally recording images and/or video and/or other data from the hardware components (e.g., from one or more IMUs, from the camera, etc.) during movement between the acquisition locations. At the next acquisition location, the mobile185and/or camera device(s)184may similarly capture a 360° panorama image and/or other type of image from that acquisition location. This process may repeat for some or all rooms of the building and in some cases external to the building, as illustrated for additional acquisition locations210C-210P in this example, with the images from acquisition locations210A to210-O being captured in a single image acquisition session in this example (e.g., in a substantially continuous manner, such as within a total of 5 minutes or 15 minutes), and with the image from acquisition location210P optionally being acquired at a different time (e.g., from a street adjacent to the building or front yard of the building). In this example, multiple of the acquisition locations210K-210P are external to but associated with the building198on the surrounding property241, including acquisition locations210L and210M in one or more additional structures189on the same property (e.g., an ADU, or accessory dwelling unit; a garage; a shed; etc.), acquisition location210K on an external deck or patio186, and acquisition locations210N-210P at multiple yard locations on the property241(e.g., backyard187, side yard188, front yard including acquisition location210P, etc.). The acquired images for each acquisition location may be further analyzed, including in some embodiments to render or otherwise place each panorama image in an equirectangular format, whether at the time of image acquisition or later, as well as further analyzed by the MIGM and/or BVGUM systems in the manners described herein.

FIG.1Billustrates examples of types of building description information110bthat may be available in some embodiments, such as existing building information that is subsequently analyzed and used by the BVGUM system. In the example ofFIG.1B, the building description information110bincludes an overview textual description, and well as various attribute data, such as may be used in part or in whole as listing information for an MLS system. In this example, the attribute data is grouped into sections (e.g., overview attributes, further interior detail attributes, further property detail attributes, etc.), but in other embodiments the attribute data may not be grouped or may be grouped in other manners, or more generally the building description information may not be separated into a list of attributes and a separate textual overview description. In this example, the separate textual overview description emphasizes characteristics that may be of interest to viewers, such as a house style type, information of interest about rooms and other building characteristics (e.g., have been recently updated or have other characteristics of interest), information of interest about the property and surrounding neighborhood or other environment, etc. In addition, in this example, the attribute data includes objective attributes of a variety of types about rooms and the building and limited information about appliances, but may lack details of various types shown in italics in this example (e.g., about subjective attributes, about inter-room connectivity and other adjacency, about other particular structural elements or objects and about attributes of such objects, etc.), such as may instead be determined by the BVGUM system via analysis of building images and/or other building information (e.g., floor plans).

Various details are provided with respect toFIGS.1A and1B, but it will be appreciated that the provided details are non-exclusive examples included for illustrative purposes, and other embodiments may be performed in other manners without some or all such details.

FIGS.2A-2Villustrate examples of automatically generating videos with information about buildings from automated analysis of building images and other building information for subsequent use in one or more automated manners, such as for the building198.

In particular,FIG.2Aillustrates an example image250a, such as a non-panorama perspective image taken in a northeasterly direction from acquisition location210B in the living room of house198ofFIG.1A(or a northeasterly facing subset view of a 360° panorama image taken from that acquisition location and formatted in a rectilinear manner)—the directional indicator109ais further displayed in this example to illustrate the northeasterly direction in which the image is taken. In the illustrated example, the displayed image includes built-in elements (e.g., light fixture130a, two windows196-1, etc.), furniture (e.g., chair192-1), and a picture194-1hanging on the north wall of the living room. No inter-room passages into or out of the living room (e.g., doorways or other wall openings) are visible in this image. However, multiple room borders are visible in the image250a, including horizontal wall-ceiling and wall-floor borders between a visible portion of the north wall of the living room and the living room's ceiling and floor, horizontal wall-ceiling and wall-floor borders between a visible portion of the east wall of the living room and the living room's ceiling and floor, and the inter-wall vertical border195-2between the north and east walls.

FIG.2Bcontinues the example ofFIG.2A, and illustrates an additional perspective image250btaken in a northwesterly direction from acquisition location210B in the living room of house198ofFIG.1A—the directional indicator109bis further displayed to illustrate the northwesterly direction in which the image is taken. In this example image, a small portion of one of the windows196-1continues to be visible, along with a portion of window196-2and a new lighting fixture130b. In addition, horizontal and vertical room borders are visible in image250bin a manner similar to that ofFIG.2A.

FIG.2Ccontinues the examples ofFIGS.2A-2B, and illustrates a third perspective image250ctaken in a southwesterly direction in the living room of house198ofFIG.1A, such as from acquisition location210B—the directional indicator109cis further displayed to illustrate the southwesterly direction in which the image is taken. In this example image, a portion of window196-2continues to be visible, as is a couch191and visual horizontal and vertical room borders in a manner similar to that ofFIGS.2A and2B. This example image further illustrates two inter-room passages for the living room, which in this example include a doorway190-1with a swinging door to enter and leave the living room (whichFIG.1Aidentifies as a door to the exterior of the house, such as the front yard), and a doorway190-6with a sliding door to move between the living room and side yard188—as is shown in the information inFIG.1A, an additional non-doorway wall opening263aexists in the east wall of the living room to move between the living room and hallway, but is not visible in the images250a-250c. It will be appreciated that a variety of other perspective images may be taken from acquisition location210B and/or other acquisition locations and displayed in a similar manner.

FIG.2Dcontinues the examples ofFIGS.2A-2C, and illustrates a 360° panorama image255d(e.g., taken from acquisition location210B), which displays the entire living room in an equirectangular format—since the panorama image does not have a direction in the same manner as the perspective images ofFIGS.2A-2C, the directional indicator109is not displayed inFIG.2D, although pose information for the panorama image may include one or more associated directions (e.g., a starting and/or ending direction for the panorama image, such as if acquired via rotation). A portion of the visual data of panorama image255dcorresponds to the first perspective image250a(shown in approximately the center portion of the image250d), while the left portion of the image255dand the far-right portion of the image255dcontain visual data corresponding to those of the perspective images250band250c—thus, for example, starting from image255d, a series of perspective images may be rendered (e.g., for use in a video) that include some or all of images250a-250c(and optionally a large quantity of intermediate images, such as to have, for example, 150 rendered perspective images to correspond to 5 seconds of panning and/or tilting within image255dif a resulting video uses 30 frames per second). This example panorama image255dincludes windows196-1,196-2and196-3, furniture191-193, doorways190-1and190-6, and non-doorway wall opening263ato the hallway room (with the opening showing part of a doorway190-3visible in the adjacent hallway). Image255dfurther illustrates a variety of room borders in a manner similar to that of the perspective images, but with the horizontal borders being displayed in an increasingly curved manner the farther they are from a horizontal midline of the image—the visible borders include vertical inter-wall borders195-1through195-4, vertical border195-5at the north/left side of the hallway opening, vertical borders at the south/right side of the hallway opening, and horizontal borders between the walls and the floor and between the walls and the ceiling.

FIG.2Dfurther illustrates information that includes one example230dof part of a 2D floor plan for the house198(e.g., corresponding to story 1 of the house), such as may be presented to an end-user in a GUI260d, with the living room being the most westward room of the house (as reflected by directional indicator209)—it will be appreciated that a 3D or 2.50 floor plan with rendered wall height information may be similarly generated and displayed in some embodiments, whether in addition to or instead of such a 2D floor plan. Various types of information are illustrated on the 2D floor plan230din this example. For example, such types of information may include one or more of the following: room labels added to some or all rooms (e.g., “living room” for the living room); room dimensions added for some or all rooms; visual indications of objects such as installed fixtures or appliances (e.g., kitchen appliances, bathroom items, etc.) or other built-in elements (e.g., a kitchen island) added for some or all rooms, optionally with associated labels and/or descriptive annotations (e.g., dual steel kitchen sink, kitchen island with red Corian surface, LED track lighting, white tile floor, etc.); visual indications added for some or all rooms of positions of additional types of associated and linked information (e.g., of other panorama images and/or perspective images that an end-user may select for further display; of audio or non-audio annotations that an end-user may select for further presentation, such as “the kitchen includes a Brand X refrigerator with features Y, a built-in stove/oven of Brand Z, etc.”; of sound recordings that an end-user may select for further presentation, such as to hear the level of street noise from bedroom1; etc.); visual indications added for some or all rooms of structural elements such as doors and windows; visual indications of visual appearance information (e.g., color and/or material type and/or texture for installed items such as floor coverings or wall coverings or surface coverings); visual indications of views from particular windows or other building locations and/or of other information external to the building (e.g., a type of an external space; items present in an external space; other associated buildings or structures, such as sheds, garages, pools, decks, patios, walkways, gardens, etc.); a key or legend269identifying visual indicators used for one or more types of information; etc. When displayed as part of a GUI such as260d, some or all such illustrated information may be user-selectable controls (or be associated with such controls) that allows an end-user to select and display some or all of the associated information (e.g., to select the 360° panorama image indicator for acquisition location210B to view some or all of that panorama image (e.g., in a manner similar to that ofFIGS.2A-2D). In addition, in this example a user-selectable control228is added to indicate a current story that is displayed for the floor plan, and to allow the end-user to select a different story to be displayed—in some embodiments, a change in stories or other levels may also be made directly from the floor plan, such as via selection of a corresponding connecting passage in the illustrated floor plan (e.g., the stairs to story 2). It will be appreciated that a variety of other types of information may be added in some embodiments, that some of the illustrated types of information may not be provided in some embodiments, and that visual indications of and user selections of linked and associated information may be displayed and selected in other manners in other embodiments.

Additional details related to embodiments of a system providing at least some such functionality of an MIGM system or related system for generating floor plans and associated information and/or presenting floor plans and associated information are included in co-pending U.S. Non-Provisional patent application Ser. No. 16/190,162, filed Nov. 14, 2018 and entitled “Automated Mapping Information Generation From Inter-Connected Images” (which includes disclosure of an example Floor Map Generation Manager, or FMGM, system that is generally directed to automated operations for generating and displaying a floor map or other floor plan of a building using images acquired in and around the building); in U.S. Non-Provisional patent application Ser. No. 16/681,787, filed Nov. 12, 2019 and entitled “Presenting Integrated Building Information Using Three-Dimensional Building Models” (which includes disclosure of an example FMGM system that is generally directed to automated operations for displaying a floor map or other floor plan of a building and associated information); in U.S. Non-Provisional patent application Ser. No. 16/841,581, filed Apr. 6, 2020 and entitled “Providing Simulated Lighting Information For Three-Dimensional Building Models” (which includes disclosure of an example FMGM system that is generally directed to automated operations for displaying a floor map or other floor plan of a building and associated information); in U.S. Provisional Patent Application No. 62/927,032, filed Oct. 28, 2019 and entitled “Generating Floor Maps For Buildings From Automated Analysis Of Video Of The Buildings' Interiors” (which includes disclosure of an example Video-To-Floor Map, or VTFM, system that is generally directed to automated operations for generating a floor map or other floor plan of a building using video data acquired in and around the building); in U.S. Non-Provisional patent application Ser. No. 16/807,135, filed Mar. 2, 2020 and entitled “Automated Tools For Generating Mapping Information For Buildings” (which includes disclosure of an example MIGM system that is generally directed to automated operations for generating a floor map or other floor plan of a building using images acquired in and around the building); and in U.S. Non-Provisional patent application Ser. No. 17/013,323, filed Sep. 4, 2020 and entitled “Automated Analysis Of Image Contents To Determine The Acquisition Location Of The Image” (which includes disclosure of an example MIGM system that is generally directed to automated operations for generating a floor map or other floor plan of a building using images acquired in and around the building, and an example ILMM system for determining the acquisition location of an image on a floor plan based at least in part on an analysis of the image's contents); each of which is incorporated herein by reference in its entirety.

FIGS.2E and2Fcontinue the examples ofFIGS.2A-2D, withFIG.2Eillustrating information255ethat includes an image250e1of the southwest portion of the living room (in a manner similar to a portion of image250cofFIG.2C), and with additional information overlaid on the image to illustrate information determined about objects and other attributes in that portion of the room from automated analysis of visual data of the image, along with information about locations of those objects. In particular, in this example, the west window (element196-2of image250c) has been identified as an object in the room, with the corresponding ‘west window’ label246p2having been determined for the object (whether automatically or based at least in part on information provided by one or more associated users), and an automatically determined location199bof the object in the image being shown (which in this example is a bounding box for the object). The information255efurther illustrates a list248pof objects and additional attributes of interest identified based at least in part on the visual data of image250e1, which indicates that visual characteristics of the west window include its type (e.g., a picture window), type of latch hardware, information about a view through the window, and optionally various other attributes (e.g., size, orientation/direction it faces, etc.). The image250e1further indicates that the doorway190-1has been identified as an object in the room, with a ‘front door’ label246p1(whether determined automatically or based at least in part on information provided by one or more associated users) and automatically determined bounding box location199abeing shown. In addition, the information248pindicates that further attributes include determined visual characteristics of the door such as a type of the door and information about door's doorknob and door hinges, which are further visually indicated131pon the image250p.FIG.2Fillustrates additional visual data that may be extracted from images250e1and/or250e2ofFIG.2Eas part of determining objects and other attributes for the room, and in particular includes close-up example images250f1,250f2and250f3corresponding to the front door for doorway190-1and its hardware131p, such as for use in determining corresponding attributes for the front door—in some embodiments and situations, if image250e1is selected for use in a video for house198and the front door and its visual characteristics are determined to be of interest, visual data of image250e1(or of another image in the living room) may be selected for display that include such images250f1,250f2and250f3(e.g., via zooming, panning, tilting, etc.) to highlight those objects and other attributes in the video while corresponding narration provides a description of those objects and other attributes. Other objects may similarly be identified, such as one or more ceiling light fixtures, furniture, walls and other surfaces, etc. (e.g., based at least in part on a list of defined types of objects that are expected or typical for rooms of type ‘living room’), and optionally described via associated narration in a generated video, and with the selected visual data of corresponding image(s) showing such objects, optionally in a manner to highlight or otherwise emphasize them (e.g., by zooming in to prominently show the objects). In addition, a ‘living room’ label246p3for the room is also determined (whether automatically or based at least in part on information provided by one or more associated users) and shown.FIG.2Efurther provides an alternative or additional image250e2, which in this example is a panorama image with 360° of visual coverage of the living room (in a manner similar to image255dofFIG.2D)—such a panorama image may be used instead of or in addition to a perspective image such as image250e1for determining objects and other attributes and additional related information (e.g., locations, labels, annotations, etc.), as well as for assessing an overall layout of items in the room and/or expected traffic flow for the room, with the example panorama image250e2similarly showing the location bounding boxes199aand199bfor the front door and west window objects, as well as additional location bounding boxes199cfor table193,199dfor ceiling light130b, and199efor the east wall—it will be appreciated that a variety of other types of objects and/or other attributes may be determined in other embodiments, including for other walls and surfaces (e.g., the ceiling and floor) and other structural elements (e.g., windows196-1and193, doorway190-6, non-doorway wall opening263a, etc.), other furniture (e.g., couch191, chair192, etc.), etc.

FIG.2Gcontinue the examples ofFIGS.2A-2F, and provide examples of additional data that may be determined about objects and other attributes for other rooms of the building based at least in part on analysis of one or more initial room-level images of those other rooms. In particular,FIG.2Gillustrates information255gthat includes an image250g1, such as for bathroom1. In a manner analogous to that of the images ofFIG.2E, image250g1includes indications131vof objects in the bathroom that are identified and for which corresponding attribute data is determined (e.g., corresponding to visual characteristics of the objects), which in this example includes a tile floor, a sink countertop, a sink faucet and/or other sink hardware, a bathtub faucet and/or other bathtub hardware, a toilet, etc.—however, location information, labels and provided instructions are not illustrated in this example. In a similar manner, image250g2for the kitchen includes indications131wof objects in the kitchen that are identified and for which corresponding attribute data is determined (e.g., corresponding to visual characteristics of the objects), which in this example includes a refrigerator, a stove on a kitchen island, a sink faucet and/or other sink hardware, a countertop and/or backsplash beside the sink, etc.—however, location information, labels and provided instructions are not illustrated in this example. It will be appreciated that various other types of objects and other attributes may be determined in these and other rooms, and further used to generate corresponding building description information, and that these types of data illustrated inFIGS.2E-2Gare non-exclusive examples provided for the purpose of illustration.

FIGS.2H-2Kcontinue the examples ofFIGS.2A-2G, and provide additional information related to analyzing floor plan information to determine additional attributes for the building. In particular,FIG.2Hillustrates information290hthat includes an example 2D floor plan230hof the building, which includes determined information222habout expected movement flow pattern attributes through the building, as indicated using corresponding labels221h, and with such information222hoptionally displayed on the floor plan (e.g., overlaid on the floor plan). In a similar manner,FIG.2Iprovides additional information290irelated to analyzing floor plan230ito determine information about various types of subjective attributes for the building (e.g., wheelchair accessibility, accessibility for people with limited walking mobility, open floor plan, a typical layout, a modern style, etc.), as indicated using corresponding labels221i, but without corresponding locations or other indications being shown on the floor plan in this example, although such corresponding locations may be determined and indicated in other embodiments and situations.FIG.2Jsimilarly provides additional information290jrelated to analyzing floor plan230jto determine information about areas of the building that correspond to public and private space attributes222j, as indicated using corresponding labels221j, and with such information222joptionally displayed on the floor plan (e.g., overlaid on the floor plan). In addition,FIG.2Kprovides additional information290krelated to analyzing floor plan230kto determine information about room type and/or functionality attributes222kof the building (e.g., bedroom, bathroom, kitchen, dining, family room, closet, etc.), as indicated using corresponding labels221k, and with such information222koptionally displayed on the floor plan (e.g., overlaid on the floor plan). It will be appreciated that particular attributes about rooms and/or the building as a whole may be determined from analysis of such a floor plan in various manners, and that the types of information illustrated inFIGS.2H-2Kare non-exclusive examples provided for the purpose of illustration, such that similar and/or other types of information may be determined in other manners in other embodiments.

FIG.2Lcontinues the examples ofFIGS.2A-2K, and illustrates information290lthat corresponds to building198. In particular, while path115illustrates a sequence in which images are acquired at locations210A through210-O for the building, the BVGUM system may determine a different sequence225lof images whose visual data will be included in a corresponding order in a video to be generated, optionally for a subset of the acquired images that are selected. In this example, a sequence of panorama images acquired at acquisition locations210A,210C,210G,210J,210K, etc. are selected, such as to correspond to beginning at the entrance to the house but quickly proceeding to illustrate information about the kitchen (e.g., due to kitchens being determined to generally be of high interest, to be of high interest to a particular recipient for whom the video is being generated, to correspond to configuration settings or other instructions regarding generation of the video, based on an order in which one or more users acquire non-panorama images and/or other information in rooms of the building, etc.). While not illustrated inFIG.2L, images acquired at additional acquisition locations may be selected for use in the same or different videos for house198, such as to have different videos or later segments within the same video to show other types of information (e.g., one or more specific rooms, such as bathrooms and/or bedrooms; one or more groups of rooms, such as corresponding to different stories or to public or private spaces; etc.). In at least some embodiments, a group of one or more images selected for use in a video and/or a determined sequence of multiple such selected images may be determined using one or more trained machine learning models (e.g., one or more neural network models).

FIGS.2M and2Ncontinue the examples ofFIGS.2A-2L, and illustrate information about selecting visual data of an image for inclusion in the visual portion of a video being generated. In particular,FIG.2Millustrates image255min a manner similar to that of image255d, but with various objects indicated to be noted in the video, including to select visual data of image255mto include in the video that shows those objects. In this example, selected objects include the vaulted ceiling as indicated299f, track lighting130bas indicated299d, front door190-1as indicated299a, west picture window196-2as indicated299b, south window196-3as indicated299g, sliding door190-6as indicated299h, east wall as indicated299e, table193as indicated299c, etc.—the arrow flow between those selected objects may, for example, indicate an order in which corresponding subsets of visual data of image255mare successively shown in the video to highlight those objects (e.g., in the form of a DAG, or Directed Acyclic Graph).FIG.2Mfurther illustrates example textual information265mthat may be included as narration within the video about this room and the selected objects (e.g., audible narration in an audio portion of the video; textual narration, such as for use with closed captioning; etc.), such as to describe attributes corresponding to objects such as the vaulted ceiling, track lighting130b, front door190-1(e.g., door type, door hardware, a description of where the door leads, etc.), west picture window196-2(e.g., window type, hardware, view, direction, etc.), south window196-3, sliding door190-6, east wall (e.g., color, type of surface material, etc.), table193(e.g., material, size, etc.), etc.FIG.2Nfurther illustrates a sequence242of visual data subsets (e.g., video frames) rendered from panorama image255mthat may be included in the video to be generated, such as to correspond to panning and/or tilting within the panorama image. In at least some embodiments, descriptions of objects and other attributes are generated using one or more trained language models.

As one non-exclusive example, the selected visual data subsets from a selected image may be from a single point of view and one or more viewing perspectives (e.g., the acquisition location of that image and in a determined direction with a determined level of zero or more zooming, such as with each frame of the video corresponding to a perspective image subset of the selected image corresponding to that viewing perspective), whether continuous or discontinuous. If multiple images are selected to be used in a video (e.g., in a determined sequence), the selected visual data from those multiple images may correspond to multiple points of view and one or more viewing perspectives for each such point of view (e.g., to select visual data from each image from its acquisition location and in one or more viewing perspective, such as with each frame of the video corresponding to a perspective image portion of a selected image, and with one or more perspective image portions used for each selected image), whether continuous or discontinuous. With respect to identifying objects to describe in the video, the BVGUM system may in at least some embodiments and situations create a graph (e.g., a GCN, or Graph Convolutional Network, that is used to learn a DAG having edges with directions and order) that includes information about one or more of the following: which objects and/or other attributes to describe, and optionally for how long; a sequence of the objects and/or other attributes to describe within an image; a determined sequence of multiple images to use; cinematographic transitions or other types of transitions between visual data of adjacent images in the determined sequence; etc.

With respect to generating the textual descriptions used for the video narration, the BVGUM system may in at least some embodiments and situations use one or more trained language models to conduct visual storytelling, image captioning, and text-image retrieval, with the text generated from the images or sequence of images being combined and/or summarized in at least some embodiments and situations (e.g., to manipulate the style, grammar, and/or modality, such as to deliver a rich and impactful recipient experience; to produce multiple generated texts; etc.), whether using multiple discrete models or be a single end-to-end model. In at least some embodiments and situations, the one or more trained language models may include one or more trained Vision and Language (VLM) models, such as large models that are trained to generate a description/caption for an input image using a large corpus of training tuples (e.g., Image, Caption tuples)—some benefits of VLM models include that there is no need to explicitly prompt the model regarding the entities you would like it to describe, which often results in descriptions that are more abstract and compelling. In at least some embodiments and situations, the one or more trained language models may further include at least one of pretrained language models, knowledge-enhanced language models, parsing and/or labeling and/or classification models (e.g., dependency parsers, constituency parsers, sentiments classifiers, semantic role labelers, etc.), algorithms used to control linguistic quality (e.g., tokenizers, lemmatizers, regular expression matching, etc.), multimodal vision and language models capable of auto-regressive or masked decoding, etc.—such labeling and/or classification models may include, for example, semantic role labelers, sentiment classifiers, and semantic classifiers to identify semantic concepts related to the entire sequence of words and tokens or any of its components (e.g., identification of semantic roles of entities in the sequence such as patient or agent as well as classifying the overall sentiment or semantics of a sequence such as how positive or negative it is about the subject, how fluent the sequence is, or how well it encourages the reader to take some action). The one or more trained language models may, for example, perform an iterative generation (decoding) of words, subwords, and tokens conditioned on prompts, prefixes, control codes, and representations of contextual information such as features derived from visual/sensor information, knowledge bases and/or graphs—parsing models may further perform operations including analyzing the internal structure of a sequence of words and tokens to identify its components in accordance with one or more grammars (e.g., dependency, context free grammar, head-driven phrase structure grammar, etc.), such as to identify modifications that can be made to a sequence of words, subwords and/or tokens to further develop a desired linguistic quality. The one or more trained language models may, for example, be organized into a directed acyclic graph providing a structure in which inputs, outputs, data sources, and models interact, with the structure being aligned with the data sources, such as with respect to one or more of the following: spatial, where the context of text generation is related to a specific point in a building such as the location or room that a panorama was taken in so that the generated text will be aligned with this location; temporal, where the context of text generation is a temporal sequence of frames in a video sequence or slideshow so that the generated text will be aligned with this sequence of frames; etc. Inputs to the one or more trained language models may include, for example, one or more of the following: structured and/or unstructured data sources (e.g., publicly or privately available, such as property records, tax records, MLS records, Wikipedia articles, homeowners association and/or covenant documents, news articles, nearby or visible landmarks, etc.) that provide information regarding the building and/or associated physical space under analysis and its surroundings and/or that provide general and commonsense information about buildings and a real estate market (e.g., housing and associated elements, overall housing market information, information related to fair housing practices, bias associated with terms and phrases that may aid in language generation, etc.); information about objects and/or other attributes (e.g., fixture types and locations, surface material, surface color, surface texture, room size, degree of natural light present in a room, walking score, expected commute times, etc.); captured and/or synthesized visual and/or sensor information along with any derivatives, such as structured and unstructured sequences (including singletons) of images, panoramas, videos, depth maps, point clouds, and segmentation maps; etc. The one or more trained language models may further be designed and/or configured to, for example, implement one or more of the following: modality, to reflect the way in which language can express relationships to reality and truth (e.g., something that is prohibited, such as “you shouldn't go to school”; advice provided through subject auxiliary inversion, such as “shouldn't you go to school?”; etc.); fluency, to reflect a measure of the natural quality of language with respect to a set of grammar rules (e.g., “big smelly brown dog” instead of “smelly brown big dog”); style, to reflect patterns of word and grammatical construction selection (e.g., short descriptions that uses interesting and engaging language; informal style, such as used for texting; formal style, such as used for an English paper or conference submission; voice, to reflect the way in which subjects and objects are organized relative to a verb (e.g., active and passive voice); etc.

As one non-exclusive example of generating text265mofFIG.2M, the BVGUM system may use a pipeline of language models and algorithms, including one or more language models from a class called “knowledge enhanced natural language generation models” (KENLG)—KENLG models are provided with a source of knowledge, and a prompt regarding the entities within the source of knowledge to which the model should attend, with the KENLG model ingesting these inputs and generating a description of those entities accordingly. There are several sources of knowledge to which KENLG models can attend including knowledge bases (KB), knowledge graphs (KG), and unstructured text like a Wikipedia page, with a knowledge base being a collection of subject-predicate-object tuples that express a predicate relationship between entities (e.g., Living Room, HasA, Fireplace; Fireplace, MadeOf, Stucco; Kitchen Counter, HasProperty, Spacious; etc.), a knowledge graph being the translation of a knowledge base into a graph structure where the nodes of the graph represent entities and the edges of the graph represent predicate relationships, and with a prompt being a sequence of entities for which a description is to be generated. For example, using the example tuples above, if the model was given the prompt (Living Room), an expected output of the model would be “the living room has a lovely stucco fireplace”—in this way, information about a house or other building that is generated by any number of one or more upstream feature extraction models and data collection processes can be aggregated into a knowledge base and then into a knowledge graph over which a KENLG model can attend. Benefits of representing a building in terms of a knowledge graph in this manner include that it represents the building in a natural way, as the composition of interrelated spaces, points of view, and objects with attributes that (when combined with a KENLG model) can be used to generate a large variety of descriptions depending on the prompt provided. For a multi-sentence description like265m, its generation may include (in part) determining a sequence of prompts to provide to the model for generation of its constituent clauses according to these entities, and then using the KENLG model to generate the text for those prompts. For example, a sequence of prompts can be identified as the entities that are visible within a sequence of points of view (e.g.,299a-hofFIG.2M), and/or information about rooms and building layout as discussed further with respect to text265oofFIG.2-0. Following the generation of a sequence of clauses corresponding to a sequence of prompts (e.g., as derived from points of view299a-h), one or more types of algorithms may be applied to perform linguistic modifications for the purpose of composing a compelling narrative, such as to include the construction of prepositional phrases to smoothly connect constituent clauses (e.g., “As you enter the living room . . . ”), to make changes to each clause's style and modality (e.g., to express ability, such as “you will notice . . . ”; to express obligation, such as “you have to see . . . ”; etc.).

With respect to selecting objects and attributes to discuss in a video being generated, such as with visual data of images that show those objects or other attributes, the objects and attributes may be selected in various manners in various embodiments. As one non-exclusive example, a group of objects and other attributes may be predefined, such as based on input from users (e.g., people interested in buying or otherwise acquiring a building or access to some or all of a building, such as renting or leasing; based on tracked activities of users in requesting or viewing information about buildings, such as in viewing images and/or other information about buildings; based on tracked activities of users in purchasing or changing objects in a building, such as during remodels; etc.)—information about such a group of objects and other attributes may be stored in various manners (e.g., in a database), and may be used for training one or more models (e.g., one or more machine learning models used to select images and/or portions of images, one or more language models used to generate textual descriptions, etc.). In other embodiments and situations, a group of objects and other attributes may be determined in other manners, whether instead of or in addition to such predefinition, such as to be learned (e.g., based at least in part on analyzing professional photos or other images of buildings to identify objects that are the focus of or otherwise included in those images).

FIG.2Nfurther illustrates information about an example of synchronizing generated narration for a video (e.g., in an audio portion of the video) with corresponding visual data in a visual portion of the video, such as for the narration to occur concurrently with the corresponding visual data or to otherwise accompany the visual data (e.g., to introduce the visual data before it is shown). As previously noted, a sequence242of visual data subsets (e.g., video frames) that may be included in the video to be generated is shown, such as to display image250bwhile accompanying narration occurs about the vaulted ceiling (e.g., around time 4.2 seconds into the narration), to display images250band/or250n1while accompanying narration occurs about the built-in track lighting (e.g., around time 8.3 seconds, such as to zoom in on the lighting in one or more images250n1), to pan counterclockwise toward the front door in one or more intermediate images250n2(e.g., between approximately times 10 and 15 seconds), to display image250n3while accompanying narration occurs about the front door (e.g., around time 15.6 seconds, such as to zoom in on some or all of the door, not shown), to display image250n4while accompanying narration occurs about the west picture window (e.g., around time 20.4 seconds, such as to zoom in on some or all of the door, etc.).

With respect to synchronizing generated narration for a video, the synchronization may be performed in various manners in various embodiments. As one non-exclusive example, as visual data is shown in a video, the narration may be presented for one or more objects and other attributes shown in the visual data. In other embodiments and situations, additional activities may be performed to generate a smooth-flowing narrative over time, such as to optimize over a combination of visual continuity and smoothness of changes in narrative topics (e.g., based at least in part by learning from a set of narrated home tour videos).

FIG.2O(referred to herein as “2-O” for clarity purposes) further illustrates information about an example of including visual data in the video corresponding to a transition between visual data of two selected images (in this example, image255macquired at acquisition location210A, and an additional image acquired at acquisition location210C, not shown). In the example ofFIG.2-O, the added visual data for the transition may include zooming from visual data shown in frame250o1to end in visual data shown in frame250o2, such as using one or more intermediate frames (not shown) to correspond to the zooming illustrated shown with arrows227(with the arrows included for the benefit of the reader, but not shown in the generated video in at least some embodiments and situations), and then further transitioning (not shown) to the additional image acquired at acquisition location210C, such as using one or more intermediate frames (not shown) to blend visual data of frame250o2to a frame including only visual data from the additional image acquired at acquisition location210C.FIG.2-O further illustrates an example of additional narration265o(e.g., to be included in an audio portion of the video) that is synchronized with the visual data of the transition(s) in the video, such as is automatically generated to describe the transition between the images' acquisition locations. In other embodiments, narration265omay be included in the video without any additional visual data corresponding to the transitions (e.g., to display frame250o1immediately followed by frame250o2at the same size), or visual data of the transition(s) may be shown in the visual portion of the video without any accompanying narration.

With respect to selecting an order for a sequence of images to include in a video, the order may be determined in various manners in various embodiments. As one non-exclusive example, some or all such images that are acquired in a building may be selected for the sequence in an order corresponding to an order in which the images were acquired, such as some or all images along the path115illustrated inFIG.1A(e.g., as may be determined based at least in part on timestamps, overlapping visual data, IMU data, etc.). As another non-exclusive example, a different order225lmay be determined, such as in a manner discussed with respect toFIG.2L. In situations in which different images are acquired at substantially different times (e.g., during multiple image acquisition sessions), images may be selected for some or all rooms on a room-by-room basis (e.g., after the images are localized within the rooms, such as may be reflected on a floor plan or other building model), whether instead of or in addition to the order in which the images were acquired and/or using a different order as noted above—the order of rooms may, for example, be determined in an order in which images were acquired in the rooms and/or in a different manner (e.g., as discussed with respect toFIG.2L). When multiple images are selected within a room, the images may in other embodiments be selected in an order corresponding to, for example, one or more of the following: an order of priority of types of features or other information shown in the images; to minimize visual jumps (and improve visual transitions) between images within a room, etc.

FIG.2Pcontinues the examples ofFIGS.2A through2-O, and illustrates information290pshowing example data flow interactions for at least some automated operations of one embodiment of the BVGUM system (referred to as the BVGUM-A system in this example). In particular, an embodiment of the BVGUM system140is illustrated executing on one or more computing systems180, and in this example embodiment receives information about a building to be analyzed that includes stored images from storage or database295, a floor plan from storage or database296, and optionally other building description information297p(e.g., a listing) and/or other information298(e.g., other building information, such as labels, annotations, etc.; other configuration settings or instructions to control generation of the video, such as a length, type of information to include, etc. information about an intended recipient of a video to be generated, such as for use in personalizing the generated video to the recipient; etc.). The input information is received in step281p, with received image information optionally being forwarded to a BVGUM image analyzer component282for analysis (e.g., to identify objects and optionally other attributes, such as local attributes specific to a particular image and/or room), with received floor plan information optionally being forwarded to a BVGUM floor plan analyzer component283for analysis (e.g., to determine other building attributes, such as global attributes corresponding to some or all of the building as a whole), and with other received building information optionally being forwarded to a BVGUM other information analyzer component284for analysis (e.g., to determine other building attributes, such as from a textual description of the building), and with the output of the components282and/or283and/or284forming some or all of the determined building attributes274for the building—in other embodiments, information about some or all such objects and/or other attributes may instead be received (e.g., as part of other information298) and included directly in building attribute information274, and as discussed in greater detail elsewhere herein, operation of such components282and/or283and/or284may include or use one or trained machine learning models.

In addition, the received images are forwarded to a BVGUM image selector and optionally sequence determiner component285pfor analysis (e.g., to determine one or more image groups each having one or more selected images to use in a corresponding video to be generated, optionally with a determined image sequence if multiple images are selected for an image group), with output of the component285pbeing one or more such image groups275p—as discussed in greater detail elsewhere herein, operation of such a component285pmay include or use one or trained machine learning models. The image groups275pand determined building attributes274are then provided to a BVGUM attribute selector and textual description generator component286p, with output of the component286pbeing generated building textual description information276p—as discussed in greater detail elsewhere herein, operation of such a component286pmay include or use one or trained language models as part of generating textual description information. In at least some embodiments, the component286pmay, for each image group, select some or all objects visible in the selected image(s) of that image group (e.g., using information from BVGUM component282and/or received in other information298) to use as attributes of the building, and optionally further select other building attributes (e.g., attributes corresponding to visual characteristics of some or all of the selected objects, such as using information from BVGUM component282and/or received in other information298; attributes corresponding to information about multiple rooms, optionally for a building as a whole or for a story or other subset of the building including with respect to a room layout for the building or building subset, and such as using information from BVGUM component283and/or received in other information298; other attributes obtained from analysis of textual descriptions of the building or other building information, such as using information from BVGUM component284and/or received in other information298; etc.). For the selected attributes for an image group, the BVGUM component286pmay then generate a textual description of each such attribute, and then combine the attribute descriptions to form an overall textual description of the building for use with that image group.

The image group(s)275pand generated building textual description information276pare then provided to a BVGUM building video generation component287p, with output of the component287pbeing a generated building video having accompanying narrative descriptions277pfor each image group. In at least some embodiments, the component287pmay, for an image group, select visual data of the image(s) of that group to include in the video (e.g., to include according to the accompanying determined sequence, if any), such as to display visual data of objects in the selected attributes whose textual descriptions are part of the information276pfor that image group and optionally to otherwise display (e.g., highlight) visual data corresponding to other such selected attributes—as discussed in greater detail elsewhere herein, the visual data selected for a selected panorama or perspective image may be used in one or more frames of the video being generated, including to optionally use techniques such as panning, tilting, zooming, etc. and having corresponding series of groups of visual data used in successive frames, as well as to in some cases show a single group of visual data from an image (e.g., some or all of a selected perspective image, a subset of a selected panorama image, etc.) in multiple successive video frames (e.g., to show the same scene for one or more seconds). The component287pmay further select and use textual description information from the information276pfor that image group to generate narration to accompany the selected visual data of the video in a synchronized manner, such as audible narration for an audio portion of the video (e.g., using automated text-to-speech generation, obtaining and using manually supplied recording of the information for the narration, etc.), and/or textual narration to be shown visually (e.g., in a manner analogous to closed captioning). In addition, the component287pmay, for an image group having multiple selected images, add additional information corresponding to transitions between the visual data of different images, such as additional visual data using one or more types of cinematographic transitions and/or additional narration to describe the transition.

In some embodiments, the BVGUM system may further include a BVGUM building matcher component288, such as to receive the determined building attributes274and/or the generated building description information276pand/or information about the selected images of the image groups275p, and to use that information to identify that the current building and/or one or more generated videos for the building match one or more specified criteria (e.g., at a later time after the generation of the information274-276pand optionally277p, such as upon receipt of corresponding criteria from one or more client computing systems182over one or more networks170)—if so, the component288produces matching building information279that may include information about the building, such as one or more of the generated building videos277pand optionally some or all of the building information274and/or276p. After one or more of these types of information277p,274and/or276pare generated, the BVGUM system may further perform step289to display or otherwise provide some or all of the generated and/or determined information, such as to transmit such information over the networks170to one or more client computing systems182for display (e.g., to present a video277p, such as by displaying the visual portion of the video and optionally playing a synchronized audio portion of the video), to one or more remote storage systems181for storage, or otherwise to one or more other recipients for further use. Additional details are included elsewhere herein regarding operations of the various BVGUM system components, and of the corresponding types of information that is analyzed and generated.

FIG.2Qcontinues the examples ofFIGS.2A-2P, and illustrates information290qshowing an example of a floor plan for building198and the surrounding yard187and188on the property on which the building is located, in a manner similar to that ofFIG.2L(including indications of the positions of acquisition locations210A to210-O of the building images, but without details of the sequence225lof images or the path115traveled while acquiring the building images), and with an additional path225qillustrated (starting at beginning location225q-aand ending at ending location225q-b) for an existing building video that has been captured by a camera device (not shown) traveling along the path through a portion of the building and onto external area186/187—it will be appreciated that such a path may travel through zero or one or multiple of the acquisition locations. In this example, various information from the floor plan may be used to generate one or more additional videos that each corresponds to a subset of the existing building video. As one non-exclusive example, an additional video may be generated to include visual data of the kitchen/dining room great room, such as to correspond to the subset224qof the path225q. Such a subset224qmay be automatically determined by the BVGUM system in various manners in some embodiments, as discussed in greater detail elsewhere herein, with non-exclusive examples including the BVGUM system analyzing visual data of the existing building video to determine one or more types of resulting information and determining to generate an additional video for each of one or more rooms or other areas through which the path passes, and with non-exclusive examples of determined types of resulting information including one or more of the following: the positions of the existing video's path (e.g., to enable the generation of the visual representation of the path225q); transitions between rooms (e.g., transitions223qin and/or out of the kitchen/dining room great room); a movement pattern226qof the path that is associated with a room or otherwise satisfies one or more defined criteria; the existing of building attributes visible in the visual data of the existing video (e.g., building attributes229qin the kitchen/dining room great room, which include various objects and surfaces in the kitchen in this example); etc. Such a subset224qmay be selected by one or more users in various manners in some embodiments, whether instead of or in addition to an automated determination by the BVGUM system and as discussed in greater detail elsewhere herein, with non-exclusive examples including a user (not shown) using a displayed GUI (not shown) that includes some or all of the information290qto select or otherwise specify the kitchen/dining room great room, and/or to select or otherwise specify a building area that includes the kitchen/dining room great room, and/or to select or otherwise specify the portion of the path225qin the kitchen/dining room great room, and/or to select or otherwise specify one or more building attributes in the kitchen/dining room great room (e.g., one or more of building attributes229q), etc. With respect to the generated additional video corresponding to path subset224qand the corresponding subset of the existing building video, it may be further presented to one or more users once generated. In addition, once the additional building video corresponding to the kitchen/dining room great room is generated, various types of information may be associated with it, such as a visual representation of and/or other information about a path associated with it, one or more labels for one or more rooms or other areas for which the video includes visual data (e.g., ‘kitchen/dining room’ in this example), one or more building attributes for which the video has visual data, etc.

FIG.2Rcontinues the examples ofFIGS.2A-2Q, and illustrates an example of a GUI260rthat may be displayed to one or more users to include at least some of information290qofFIG.2Q, such as to enable user selection or other specification of segment criteria to identify the subset of the existing building video to use in generating the additional building video corresponding to path subset224qbased on floor plan information presented to the user—in this example, the floor plan230rshown in the GUI260rincludes a visual representation225rof the path of the existing building video, but does not include information overlaid on the floor plan about the acquisition locations210A to210-O or the building attributes229q, while including additional displayed information231rand232rin association with the floor plan to list pieces of media for the building grouped by room and at least some of the determined building attributes grouped by room, respectively. In this example, a user (not shown) may, for example, specify to generate the additional video corresponding to the kitchen/dining room great room by one or more of the following: selecting233rthe kitchen/dining room great room, such as by clicking within the room on the floor plan if the rooms are individually selectable, or by drawing a shape (not shown) around the kitchen/dining room great room, or by selecting the kitchen/dining room great room from a displayed list of rooms (not shown), etc.; selecting232r1or otherwise specifying one or more building attributes in the kitchen/dining room great room (e.g., from the list232rof building attributes grouped by room; by selecting visual representations of the building attributes overlaid on the floor plan, not shown, or otherwise specifying the building attributes; etc.); etc. In addition, one or more existing and/or generated videos associated with a building floor plan may be displayed on or otherwise in association with a floor plan in various manners in various embodiments, such as to overlay a visual representation of a path of a video on the floor plan, to display an icon (e.g., user-selectable) representing some or all of the video in each room through which the video's path passes, to display a list of videos (e.g., grouped by room or other area), etc.

FIG.2Scontinues the examples ofFIGS.2A-2R, and illustrates information290sshowing example data flow interactions for at least some automated operations of one embodiment of the BVGUM system (referred to as the BVGUM-B system in this example). In particular, an embodiment of the BVGUM system140is illustrated executing on one or more computing systems180, and in this example embodiment receives information about a building to be analyzed that includes stored images from storage or database295, a floor plan from storage or database296, one or more existing building videos from storage or database297s, and optionally other information298(e.g., other building information, such as labels, annotations, etc.; other configuration settings or instructions to control generation of the video, such as a length, type of information to include, etc.; etc.). The input information is received in step281s, with received video information being forwarded to a BVGUM video analyzer component291sfor analysis (e.g., to identify an associated path or other localization information for one or more video locations; objects and optionally other building attributes visible in the visual data of the vide; etc.), with received floor plan information being forwarded to a BVGUM floor plan analyzer component283for analysis (e.g., to determine building attributes from the floor plan, such as global attributes corresponding to some or all of the building as a whole and/or specific to particular rooms or other areas), with received images optionally being forwarded to a BVGUM image analyzer component282for analysis in a manner similar to that ofFIG.2P, and with other received building information optionally being forwarded to a BVGUM other information analyzer component284for analysis in a manner similar to that ofFIG.2P, and with the output of the components282and/or283and/or284forming some or all of the determined building attributes274for the building, and the output of the component291sforming the determined video attributes292—in other embodiments, information about some or all such video attributes and/or about objects and other building attributes may instead be received (e.g., as part of other information298) and included directly in video attribute information292and building attribute information274, respectively, and as discussed in greater detail elsewhere herein, operation of such components291sand/or282and/or283and/or284may include or use one or trained machine learning models. The determined building attributes274are then optionally provided to a BVGUM attribute selector component286s, with optional output of the component286sbeing selected building attributes276s, such as to select some or all objects of one or more specified types for which to include visual data in an additional video to be generated.

The existing videos and video attributes292and determined building attributes274and optionally selected building attributes are then provided to a BVGUM video subset determiner component285s, with output of the component285sbeing one or more subsets of one or more existing videos to use for each of one or more additional videos in its generation, such as based in part or in whole on user input obtained294sby the component285sand/or received in other information298, and/or based in part or in whole by automated operations of the BVGUM system to determine one or more existing video subsets (e.g., subsets that include visual data of selected building attributes276s), as discussed in greater detail elsewhere herein. The corresponding selected existing video(s) and/or their determined subsets are then provided to a BVGUM additional video generation component287s, with output of the component287sbeing at least one generated additional video277shaving visual data for at least one subset of an existing video, including in some embodiments and situations to use a single existing building video subset as a generated additional video without further modification or manipulation, and in some embodiments and situations to sequentially use two or more existing building video subsets as a generated additional video without further modification or manipulation, and in some embodiments and situations to perform further modifications or other manipulations (e.g., cropping frames, adjusting lighting and other visual attributes, selecting a particular pose and/or zoom level to use as a subset portion of each of one or more frames, etc.) to one or more such existing video subsets as part of generation of an additional video. In addition, the component287smay, for an additional video including two or more existing video subsets, add additional information corresponding to transitions between the visual data of different subsets, such as additional visual data using one or more types of cinematographic transitions and/or narration to describe the transition. Output from the component287smay further optionally include additional information278sabout the one or more generated additional videos for presentation on or in association with a floor plan presentation, such as a visual representation of a path for a generated additional video, an indication of one or more rooms or other areas in which to include the additional video in a listing or other grouping of associated media data, etc.

In some embodiments, the BVGUM system may further include a BVGUM building matcher component288, such as to receive the determined building attributes274and/or the determined video attributes292, and to use that information to identify that the current building and/or one or more generated videos for the building match one or more specified criteria (e.g., at a later time after the generation of the information274and292and277s, such as upon receipt of corresponding criteria from one or more client computing systems182over one or more networks170)—if so, the component288produces matching building information279that may include information about the building, such as one or more of the generated additional videos277sand optionally some or all of the building information274and/or additional information278s. After one or more of these types of information277s,278s,274and/or292are generated, the BVGUM system may further perform step289to display or otherwise provide some or all of the generated and/or determined information, such as to transmit such information over the networks170to one or more client computing systems182for display (e.g., to present a video277s, such as by displaying the visual portion of the video and optionally playing a synchronized audio portion of the video), to one or more remote storage systems181for storage, or otherwise to one or more other recipients for further use. Additional details are included elsewhere herein regarding operations of the various BVGUM system components, and of the corresponding types of information that is analyzed and generated.

FIG.2Tcontinues the examples ofFIGS.2A-2S, and illustrates an example of a GUI260tthat may be displayed to one or more users in a manner similar to that ofFIG.2R, but in this example to enable user selection or other specification of information of one or more generation criteria for a new video to be generated that is not based on one or more subsets of one or more existing building videos, such as to instead be generated using visual data of a subset of the building images. In this example, a path225tis specified by a user and/or automatically determined by the BVGUM system to use in generating such a new video, with the path225toptionally not displayed to the user (e.g., at all, until after generation of the new video, until after completion of the generation criteria determination, etc.). In this example, the floor plan230tshown in the GUI260tis similar to that of GUI260rofFIG.2R, but does not include the path225rfor the existing video (e.g., due to that video not existing, such as reflected in the updated grouping of media data for the kitchen/dining room great room in information231t; due to the existing video being unrelated to the generation of the new video, etc.). However, the GUI260tdoes include one or more indications of the generation criteria for the new video to be generated, such as a user selection or automated determination233tof one or more rooms or other areas for which to include visual data in the new video (in this example, the kitchen/dining room great room, and the family room, such as by selecting the rooms on the floor plan, drawing one or more shapes on the floor plan that include the rooms, etc.), and/or a user selection or automated determination of one or more building attributes232t1for which to include visual data in the new video, and/or a user selection or automated determination of one or more building images232t2to use in generating the new video, and/or a user specification or automated determination225tof the path to use for the new video, and/or a user specification or automated determination236trof one or more building locations for which to include visual data in the new video, and/or a user specification or automated determination of orientations or poses (not shown) to use from one or more locations to include as visual data in the new video, etc.

FIG.2Ucontinues the examples ofFIGS.2A-2T, and illustrates information290ushowing an example of a floor plan for building198and the surrounding yard187and188on the property on which the building is located, in a manner similar to that ofFIG.2Q(but without indications of the positions of some of the building image acquisition locations), and with an additional path225uillustrated (starting at beginning location225u-aand ending at ending location225u-b) for a new video to be generated using visual data of at least the building images at acquisition locations210G and210J and250rand optionally further at one or more of acquisition locations210I and210K—it will be appreciated that such a path may travel through zero or one or multiple of the acquisition locations. In this example, various additional information is illustrated, such as automatically determined orientations or poses234uat various positions along the path, such as may be automatically determined by the BVGUM system to highlight building attributes that have been selected and/or determined, or to otherwise satisfy one or more defined criteria—in this example, the determined orientations or poses include performing a 360 degree circle at beginning location225u-a, then proceeding at a substantial tangent to the path for the first approximately half of the path, and then changing the orientation to pose to highlight particular attributes in the kitchen (e.g., the island, stove, refrigerator, sink, etc.) before optionally returning to a substantially tangent orientation or pose (not shown) near the end of the path and/or by completing another 360 degree circle (not shown) at the end of the path. The path and/or orientations/poses may be determined in various manners in various embodiments, as discussed in greater detail elsewhere herein.

FIG.2Vcontinues the examples ofFIGS.2A-2U, and illustrates information290vshowing example data flow interactions for at least some automated operations of one embodiment of the BVGUM system (referred to as the BVGUM-C system in this example). In particular, an embodiment of the BVGUM system140is illustrated executing on one or more computing systems180, and in this example embodiment receives information about a building to be analyzed that includes stored images from storage or database295, a floor plan from storage or database296, and optionally other information298(e.g., one or more existing building videos; other building information, such as labels, annotations, etc.; other configuration settings or instructions to control generation of the video, such as a length, type of information to include, etc.; etc.). The input information is received in step281v, with received floor plan information being forwarded to a BVGUM floor plan analyzer component283for analysis in a manner similar to that ofFIG.2S(e.g., to determine building attributes from the floor plan, such as global attributes corresponding to some or all of the building as a whole and/or specific to particular rooms or other areas), with received images optionally being forwarded to a BVGUM image analyzer component282for analysis in a manner similar to that ofFIGS.2P and2S, and with other received building information optionally being forwarded to a BVGUM other information analyzer component284for analysis in a manner similar to that ofFIGS.2P and2S, and with the output of the components282and/or283and/or284forming some or all of the determined building attributes274for the building—in other embodiments, information about some or all such objects and other building attributes may instead be received (e.g., as part of other information298) and included directly in building attribute information274, and as discussed in greater detail elsewhere herein, operation of such components282and/or283and/or284may include or use one or trained machine learning models. The determined building attributes274are then optionally provided to a BVGUM attribute selector component286v, with optional output of the component286vbeing selected building attributes276v, such as to select some or all objects of one or more specified types for which to include visual data in an additional video to be generated.

The building images and determined building attributes274and optionally selected building attributes276vare then provided to a BVGUM determiner component285vthat determines a path to use and optionally building attributes to highlight, with output of the component285vbeing provided to a BVGUM video generation component287vto use for generation of one or more new videos and optionally to a BVGUM pose determiner component291, such as based in part or in whole on user input obtained294vby the component285vand/or received in other information298, and/or based in part or in whole by automated operations of the BVGUM system to determine such information, as discussed in greater detail elsewhere herein. The BVGUM pose determiner component291, if used, may further use output of the component285vand/or user input294vto determine orientations/poses to use at each of one or more positions along the determined path, and further provide that information to the component287vfor use in the generation of the one or more new videos, with output of the component287vbeing at least one generated new video277vhaving visual data for at least a portion of one or more rooms or other areas of the building that includes visual data from images along the path or that otherwise have visual data corresponding to determined orientations/poses from the path. Output from the component287vmay further optionally include additional information278vabout the one or more generated new videos for presentation on or in association with a floor plan presentation, such as a visual representation of a path for a generated new video, an indication of one or more rooms or other areas in which to include the new video in a listing or other grouping of associated media data, etc.

In some embodiments, the BVGUM system may further include a BVGUM building matcher component288, such as to receive the determined building attributes274, and to use that information to identify that the current building and/or one or more generated videos for the building match one or more specified criteria (e.g., at a later time after the generation of the information274and277v, such as upon receipt of corresponding criteria from one or more client computing systems182over one or more networks170)—if so, the component288produces matching building information279that may include information about the building, such as one or more of the generated new videos277vand optionally some or all of the building information274and/or additional information278v. After one or more of these types of information277v,278v,274and/or292are generated, the BVGUM system may further perform step289to display or otherwise provide some or all of the generated and/or determined information, such as to transmit such information over the networks170to one or more client computing systems182for display (e.g., to present a video277v, such as by displaying the visual portion of the video and optionally playing a synchronized audio portion of the video), to one or more remote storage systems181for storage, or otherwise to one or more other recipients for further use. Additional details are included elsewhere herein regarding operations of the various BVGUM system components, and of the corresponding types of information that is analyzed and generated.

Various details have been provided with respect toFIGS.2A-2V, but it will be appreciated that the provided details are non-exclusive examples included for illustrative purposes, and other embodiments may be performed in other manners without some or all such details.

FIG.3is a block diagram illustrating an embodiment of one or more server computing systems300executing an implementation of a BVGUM system340(e.g., in a manner analogous to server computing system(s)180and BVGUM system140ofFIG.1A), and one or more server computing systems380executing an implementation of an ICA system388and an MIGM system389—the server computing system(s) and BVGUM and/or ICA and/or MIGM systems may be implemented using a plurality of hardware components that form electronic circuits suitable for and configured to, when in combined operation, perform at least some of the techniques described herein. One or more computing systems and devices may also optionally be executing a building information access system (such as server computing system(s)300) and/or optional other programs335and383(such as server computing system(s)300and380, respectively, in this example), although such as building information access system is not illustrated in this example. In the illustrated embodiment, each server computing system300includes one or more hardware central processing units (“CPUs”) or other hardware processors305, various input/output (“I/O”) components310, storage320, and memory330, with the illustrated I/O components including a display311, a network connection312, a computer-readable media drive313, and other I/O devices315(e.g., keyboards, mice or other pointing devices, microphones, speakers, GPS receivers, etc.). Each server computing system380may have similar components, although only one or more hardware processors381, memory387, storage384and I/O components382are illustrated in this example for the sake of brevity.

The server computing system(s)300and executing BVGUM system340, server computing system(s)380and executing ICA and MIGM systems388-389, and optionally executing building information access system (not shown), may communicate with each other and with other computing systems and devices in this illustrated embodiment, such as via one or more networks399(e.g., the Internet, one or more cellular telephone networks, etc.), including to interact with user client computing devices390(e.g., used to view building information such as generated building videos, building descriptions, floor plans, images and/or other related information, such as by interacting with or executing a copy of the building information access system), and/or mobile image acquisition devices360(e.g., used to acquire images and/or other information for buildings or other environments to be modeled, such as in a manner analogous to computing device185ofFIG.1A), and/or optionally other navigable devices395that receive and use floor plans and optionally other generated information for navigation purposes (e.g., for use by semi-autonomous or fully autonomous vehicles or other devices). In other embodiments, some of the described functionality may be combined in less computing systems, such as to combine the BVGUM system340and a building information access system in a single system or device, to combine the BVGUM system340and the image acquisition functionality of device(s)360in a single system or device, to combine the ICA and MIGM systems388-389and the image acquisition functionality of device(s)360in a single system or device, to combine the BVGUM system340and one or both of the ICA and MIGM systems388-389in a single system or device, to combine the BVGUM system340and the ICA and MIGM systems388-389and the image acquisition functionality of device(s)360in a single system or device, etc.

In the illustrated embodiment, an embodiment of the BVGUM system340executes in memory330of the server computing system(s)300in order to perform at least some of the described techniques, such as by using the processor(s)305to execute software instructions of the system340in a manner that configures the processor(s)305and computing system300to perform automated operations that implement those described techniques. The illustrated embodiment of the BVGUM system may include one or more components, not shown, to each perform portions of the functionality of the BVGUM system, such as in a manner discussed elsewhere herein, and the memory may further optionally execute one or more other programs335—as one specific example, a copy of the ICA and/or MIGM systems may execute as one of the other programs335in at least some embodiments, such as instead of or in addition to the ICA and/or MIGM systems388-389on the server computing system(s)380, and/or a copy of a building information access system may execute as one of the other programs335. The BVGUM system340may further, during its operation, store and/or retrieve various types of data on storage320(e.g., in one or more databases or other data structures), such as various types of user information322, floor plans and other associated information324(e.g., generated and saved 2.5D and/or 3D models, building and room dimensions for use with associated floor plans, additional images and/or annotation information, etc.), images and associated information326, generated building videos328(optionally with narrative descriptions) and other generated building information (e.g., determined building attributes, generated attribute descriptions, generated building descriptions, etc.), and/or various types of optional additional information329(e.g., various analytical information related to presentation or other use of one or more building interiors or other environments).

In addition, embodiments of the ICA and MIGM systems388-389execute in memory387of the server computing system(s)380in the illustrated embodiment in order to perform techniques related to generating panorama images and floor plans for buildings, such as by using the processor(s)381to execute software instructions of the systems388and/or389in a manner that configures the processor(s)381and computing system(s)380to perform automated operations that implement those techniques. The illustrated embodiment of the ICA and MIGM systems may include one or more components, not shown, to each perform portions of the functionality of the ICA and MIGM systems, respectively, and the memory may further optionally execute one or more other programs383. The ICA and/or MIGM systems388-389may further, during operation, store and/or retrieve various types of data on storage384(e.g., in one or more databases or other data structures), such as video and/or image information386acquired for one or more buildings (e.g., 360° video or images for analysis to generate floor plans, to provide to users of client computing devices390for display, etc.), floor plans and/or other generated mapping information387, and optionally other information385(e.g., additional images and/or annotation information for use with associated floor plans, building and room dimensions for use with associated floor plans, various analytical information related to presentation or other use of one or more building interiors or other environments, etc.)—while not illustrated inFIG.3, the ICA and/or MIGM systems may further store and use additional types of information, such as about other types of building information to be analyzed and/or provided to the BVGUM system, about ICA and/or MIGM system operator users and/or end-users, etc.

Some or all of the user client computing devices390(e.g., mobile devices), mobile image acquisition devices360, optional other navigable devices395and other computing systems (not shown) may similarly include some or all of the same types of components illustrated for server computing system300. As one non-limiting example, the mobile image acquisition devices360are each shown to include one or more hardware CPU(s)361, I/O components362, memory and/or storage367, one or more imaging systems365, IMU hardware sensors369(e.g., for use in acquisition of video and/or images, associated device movement data, etc.), and optionally other components. In the illustrated example, one or both of a browser and one or more client applications368(e.g., an application specific to the BVGUM system and/or to ICA system and/or to the MIGM system) are executing in memory367, such as to participate in communication with the BVGUM system340, ICA system388, MIGM system389and/or other computing systems. While particular components are not illustrated for the other navigable devices395or other computing devices/systems390, it will be appreciated that they may include similar and/or additional components.

It will also be appreciated that computing systems300and380and the other systems and devices included withinFIG.3are merely illustrative and are not intended to limit the scope of the present invention. The systems and/or devices may instead each include multiple interacting computing systems or devices, and may be connected to other devices that are not specifically illustrated, including via Bluetooth communication or other direct communication, through one or more networks such as the Internet, via the Web, or via one or more private networks (e.g., mobile communication networks, etc.). More generally, a device or other computing system may comprise any combination of hardware that may interact and perform the described types of functionality, optionally when programmed or otherwise configured with particular software instructions and/or data structures, including without limitation desktop or other computers (e.g., tablets, slates, etc.), database servers, network storage devices and other network devices, smart phones and other cell phones, consumer electronics, wearable devices, digital music player devices, handheld gaming devices, PDAs, wireless phones, Internet appliances, and various other consumer products that include appropriate communication capabilities. In addition, the functionality provided by the illustrated BVGUM system340may in some embodiments be distributed in various components, some of the described functionality of the BVGUM system340may not be provided, and/or other additional functionality may be provided.

It will also be appreciated that, while various items are illustrated as being stored in memory or on storage while being used, these items or portions of them may be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other embodiments some or all of the software components and/or systems may execute in memory on another device and communicate with the illustrated computing systems via inter-computer communication. Thus, in some embodiments, some or all of the described techniques may be performed by hardware means that include one or more processors and/or memory and/or storage when configured by one or more software programs (e.g., by the BVGUM system340executing on server computing systems300, by a Building Information Access system executing on server computing systems300or other computing systems/devices, etc.) and/or data structures, such as by execution of software instructions of the one or more software programs and/or by storage of such software instructions and/or data structures, and such as to perform algorithms as described in the flow charts and other disclosure herein. Furthermore, in some embodiments, some or all of the systems and/or components may be implemented or provided in other manners, such as by consisting of one or more means that are implemented partially or fully in firmware and/or hardware (e.g., rather than as a means implemented in whole or in part by software instructions that configure a particular CPU or other processor), including, but not limited to, one or more application-specific integrated circuits (ASICs), standard integrated circuits, controllers (e.g., by executing appropriate instructions, and including microcontrollers and/or embedded controllers), field-programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), etc. Some or all of the components, systems and data structures may also be stored (e.g., as software instructions or structured data) on a non-transitory computer-readable storage mediums, such as a hard disk or flash drive or other non-volatile storage device, volatile or non-volatile memory (e.g., RAM or flash RAM), a network storage device, or a portable media article (e.g., a DVD disk, a CD disk, an optical disk, a flash memory device, etc.) to be read by an appropriate drive or via an appropriate connection. The systems, components and data structures may also in some embodiments be transmitted via generated data signals (e.g., as part of a carrier wave or other analog or digital propagated signal) on a variety of computer-readable transmission mediums, including wireless-based and wired/cable-based mediums, and may take a variety of forms (e.g., as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames). Such computer program products may also take other forms in other embodiments. Accordingly, embodiments of the present disclosure may be practiced with other computer system configurations.

FIGS.4A-4Dillustrate an example embodiment of a flow diagram for a Building Video Generation and Usage Manager (BVGUM) System routine400. The routine may be performed by, for example, execution of the BVGUM system140ofFIG.1A, the BVGUM system340ofFIG.3, and/or an BVGUM system as described with respect toFIGS.2D-2Vand elsewhere herein, such as to perform automated operations related to automatically generating building videos with visual data of one or more indicated types (e.g., of user-selected and/or automatically determined building objects or other building attributes, user-selected and/or automatically determined rooms or other areas, etc.), optionally with accompanying automatically generated narration, and to subsequently using the generated building videos in one or more automated manners. In the example embodiment ofFIGS.4A-4D, the indicated buildings may be houses or other buildings, and generation of building videos includes generating description information for selected building attributes and using them for narration of a video including visual data of images acquired for the building, but in other embodiments, other types of data structures and analyses may be used for other types of structures or for non-structure locations, and the generated building information may be used in other manners than those discussed with respect to routine400, as discussed elsewhere herein.

The illustrated embodiment of the routine begins at block405, where information or instructions are received. The routine continues to block410to determine whether the instructions or other information received in block405indicate to generate one or more videos for an indicated building (e.g., based at least in part on existing images and/or videos of the indicated building), and if so the routine continues to perform some or all of blocks415-465and/or905-990to do so, and otherwise continues to block476. In block415, the routine optionally obtains configuration settings and/or information specific to a recipient to use in the video generation (e.g., information received in block405, stored information, etc.), such as corresponding to video length, types of information to include in the video (e.g., room types, object types, other attribute types, etc.). In block420, the routine then determines whether existing building information (e.g., images, a floor plan with at least 2D room shapes positioned relative to each other, one or more existing videos, a textual building description, lists or other indications of building objects and/or other building attributes, labels and/or descriptive annotations associated with images and/or rooms and/or objects, etc.) is available for the building, and if so proceeds to block422to retrieve such existing building information. If it is instead determined in block420that the building information is not available, the routine instead proceeds to perform blocks425-440to generate such images and a floor plan and associated information, including to optionally obtain available information about the building in block425(e.g., building dimensions and/or other information about the size and/or structure of the building; external images of the building, such as from overhead and/or from a nearby street; etc., such as from public sources), to initiate execution of an ICA system routine in block430to acquire images and optionally additional data for the building (with one example of such a routine illustrated inFIG.5), and to initiate execution of a MIGM system routine in block440to use the acquired images from block430to generate a floor plan and optionally additional mapping-related building data (with one example of such a routine illustrated inFIGS.6A-6B).

In blocks441-465and905-990, the routine performs several activities as part of using building information from blocks430and440or from block422to generate one or more videos for the building. In particular, in block441, the routine includes, if such information is not already available from blocks422and/or430, analyzing each image using one or more trained machine learning models (e.g., one or more trained classification neural networks) to identify structural elements and other objects, and to determine further attributes associated with such objects (e.g., color, surface material, style, locations, orientations, descriptive labels, etc.) or otherwise with the building. In block442, the routine then optionally analyzes other building information (e.g., a floor plan, textual descriptions, etc.) to determine further attributes for the building using one or more trained machine learning models (e.g., one or more trained classification neural networks), such as based at least in part on layout information (e.g., inter-connectedness and other adjacency information for groups of two or more rooms)—the determined attributes may, for example, include attributes that each classify the building floor plan according to one or more subjective factors (e.g., accessibility friendly, an open floor plan, an atypical floor plan, etc.), a type of room for some or all rooms in the building, types of inter-room connections and other adjacencies between some or all rooms (e.g., connected by a door or other opening, adjacent with an intervening wall but not otherwise connected, not adjacent, etc.), one or more objective attributes, etc.

In block443, the routine determines whether the received instructions or other information in block405indicate to generate at least one additional video using one or more existing building videos, such as by supplying corresponding segment criteria to use in the generating of the additional video(s) or otherwise indicating to generate one or more additional videos, and if so proceeds to perform blocks905-935to do so. In particular, the routine in block905retrieves one or more existing building videos (such as retrieved in block422or received in block405), and in block910proceeds to optionally present information for the indicated building in a displayed GUI in order to obtain user-specified segment criteria from one or more end-user recipients of the additional video(s) to be generated and/or other users (e.g., if not received in block405from a Building Information Access routine or other source, with one example of such a routine discussed inFIGS.7A-7B). Such building information presentation may include, for example, displaying a visual representation of the floor plan overlaid with visual representations of one or more existing videos at one or more associated positions on the floor plan (e.g., displaying a path for an existing video) or otherwise provided in association with the floor plan (e.g., to provide a list or other group of such existing building videos), and optionally information about building attributes and building images shown at associated positions on the floor plan or otherwise provided in association with the floor plan (e.g., to provide a list or other group of building images associated with a room or other area of the building, to provide a list or other group of building attributes associated with a room or other area of the building, etc.), as well as providing one or more user-selectable controls to enable the user(s) to provide one or more segment criteria for use in generating one or more additional videos from one or more existing videos. In block915, the routine optionally receives user input from at least one user (e.g., an end-user recipient to which the generated new video will be presented) to provide one or more segment criteria about aspects of the building for which to include visual data in the one or more additional videos to be generated by, for example, indicating one or more portions of one or more existing videos to use in generating a corresponding additional video (e.g., by selecting a portion of a presented visual representation of an existing video, by specifying one or more rooms or other areas for which to include visual data in a generated additional video from existing video(s), by specifying one or more building attributes for which to include visual data in a generated additional video from existing video(s), by providing a verbal description that is analyzed to identify one or more such types of data, etc.). In block920, the routine then analyzes each existing building video to associate some or all frames with a room or other area of interest, including to optionally identify building attributes such as building objects and/or structural elements (e.g., by matching to visual data of one or more building images with known locations, and/or matching to data from the floor plan, and/or identifying building objects and/or structural elements associated with a room type of a room or other area on the floor plan), to optionally detect transitions between rooms and/or other areas for use in determining where in the existing building to start or end an additional video for that room or a next room, to optionally detect movement patterns along a path of the video that are associated with a room or other area of interest or that otherwise satisfy one or more defined criteria, etc.—in addition, the routine may in some embodiments further determine whether the frames of the video have a sufficiently wide and/or tall angle of view that standard playback of the frame uses only a portion of the frame. In block925, the routine then determines aspects of the building for which to include visual data in the one or more additional videos to be generated, such as based in part or in whole using segment criteria specified in block915or received in block405, and/or based in part or in whole in an automated manner (e.g., for an existing video with a path that passes through multiple rooms or other areas of a building, to generate an additional video for each such room or other area that includes a portion of that existing video)—in addition, if the video frames are determined to have a sufficiently wide and/or tall angle of view that standard playback of the frame uses only a portion of the frame, the routine may further determine particular orientations to use from particular positions (e.g., to highlight particular building attributes of interest), such as to correspond to particular portions of at least some video frames. In block930, the routine then generates one or more additional videos with visual data for the building aspects determined in block920, with each additional video including one or more subset portions of one or more existing videos and including at least visual data from the subset portion(s), such as by using data from the analysis of the existing video(s) in block925—if the video frames are determined to have a sufficiently wide and/or tall angle of view that standard playback of the frame uses only a portion of the frame, the routine may further determine particular portions of at least some video frames of the existing video subset portions to present during playback of the generated additional video (whether by default, with a user viewing the playback able to alter the viewpoint during the playback within the frames of the generated additional video, or as the only option for playback), such as to show determined building attributes of interest. In block935, the routine then optionally generates one or more visual representations related to the one or more generated additional videos to be overlaid on or otherwise included with a floor plan of the building (e.g., a path along which the visual data of the generated video moves, a group of multiple pieces of media associated with a room or other area, a user-selectable icon within a room or other area to which the generated additional video corresponds, etc.), and optionally presents some or all of the generated additional videos and/or at least one of the generated visual representations with the floor plan to one or more end-user recipients (e.g., one or more users from which user input is received in block915), such as by updating information presented in block910.

If it is instead determined in block443not to generate one or more additional videos from one or more existing building videos, the routine continues to block444to determine whether the received instructions or other information in block405indicate to generate at least one new video using information associated with the building floor plan, such as by supplying corresponding generation criteria to use in the generating of the new video(s) or otherwise indicating to generate one or more new videos, and if so proceeds to perform blocks955-990to do so. In particular, the routine in block955optionally obtains user input from at least one user (e.g., an end-user recipient to which the generated new video will be presented) to provide one or more generation criteria about aspects of the building for which to include visual data in the one or more new videos to be generated (e.g., if not received in block405from a Building Information Access routine or other source, with one example of such a routine discussed inFIGS.7A-7B), such as using user selections of the aspects from a displayed GUI with building information and/or from verbal input. The displayed GUI may include, for example, displaying a visual representation of the floor plan overlaid with visual representations of one or more images at one or more associated positions on the floor plan or otherwise provided in association with the floor plan (e.g., to provide a list or other group of such images), and optionally information about building attributes shown at associated positions on the floor plan or otherwise provided in association with the floor plan (e.g., to provide a list or other group of building attributes associated with a room or other area of the building, etc.), as well as providing one or more user-selectable controls to enable the user(s) to provide one or more generation criteria for use in generating one or more new videos using visual data of images and structural information about the building from the floor plan. In addition, the user input may be based, for example, on indicating one or more images having visual data to use in generating a corresponding new video (e.g., by directly selecting the images, by specifying one or more rooms or other areas for which to include visual data in a generated new video and in which the indicated images have corresponding acquisition locations or otherwise include such visual data, by specifying one or more building attributes for which to include visual data in a generated new video and for which the indicated images have corresponding visual data, by providing a verbal description that is analyzed to identify one or more such images, etc.), and/or by indicating one or more rooms or other area for which the new video(s) will have visual data, such as by directly selecting the one or more rooms or other areas, specifying a path that the visual data of the new video follows through the one or more rooms or other areas, etc. In block960, the routine then determines one or more areas of the building for which to include visual data in the one or more new videos to be generated that include one or more rooms or other areas, such as based in part or in whole using generation criteria specified in block955or received in block405, and/or based in part or in whole in an automated manner (e.g., a room or other area through which an indicated path passes, a room or other areas that includes one or more indicated building attributes, etc.). In block965, the routine then determines aspects of the building in the determined building area(s) for which to include visual data in the one or more new videos to be generated (e.g., one or more building attributes for which to include visual data), such as based in part or in whole using generation criteria specified in block955or received in block405, and/or based in part or in whole in an automated manner (e.g., to determine a path through an indicated room or other area that is likely or plausible for a human videographer to follow and/or that is predicted to provide results satisfying one or more defined criteria; to determine one or more orientations from one or more locations for which to include visual data, such as to point toward particular selected building attributes; etc., and optionally using one or more trained machine learning models)—in addition, if video frames to be included in the new video(s) are sufficiently wide and/or tall angle of view that standard playback of the frame uses only a portion of the frame, the routine may further determine particular orientations to use from particular positions (e.g., to highlight particular building attributes of interest). In block970, the routine then determines building images with acquisition locations in the determined building area or that otherwise include visual data for at least some of the determined building area, and in block975proceeds to generate one or more new videos with visual data for the determined area and to correspond to the determined aspects, such as by using NeRF and/or SfM generation techniques based on the visual data of the determined building images, and optionally to include corresponding generated narration with respect to the determined building area and/or building attributes and other determined aspects as discussed in greater detail elsewhere herein—if some or all frames of the generated new video(s) have a sufficiently wide and/or tall angle of view that standard playback of the frame uses only a portion of the frame, the routine may further determine particular portions of at least some video frames of the new video(s) to present during playback of the generated new video (whether by default, with a user viewing the playback able to alter the viewpoint during the playback within the frames of the generated new video, or as the only option for playback), such as to show determined building attributes of interest. In block990, the routine then optionally generates one or more visual representations related to the one or more generated new videos to be overlaid on or otherwise included with a floor plan of the building (e.g., a path along which the visual data of the generated video moves, a group of multiple pieces of media associated with a room or other area, a user-selectable icon within a room or other area to which the generated new video corresponds, etc.), and optionally presents some or all of the generated new videos and/or at least one of the generated visual representations with the floor plan to one or more end-user recipients (e.g., one or more users from which user input is received in block955, such as by updating information presented in block955).

If it is instead determined in block444not to generate one or more new videos using information associated with the building floor plan, the routine continues to block446to analyze the images and optionally other building information to determine one or more image groups that each includes one or more selected videos and optionally a determined sequence of multiple selected images, such as using one or more trained machine learning models (e.g., one or more neural networks) and in accordance with any configuration settings and/or recipient information from block415. After block446, the routine continues to block450to select objects and optionally other attributes that correspond to the selected images to describe in the one or more videos being generated (e.g., to determine, for each of some or all of the selected images, objects visible in the image and optionally positions of the objects within the image, and to optionally select further building attributes determined in block444and/or obtained in block422), such as in accordance with any configuration settings and/or recipient information from block415, and optionally using one or more trained machine learning models (e.g., one or more neural networks), whether the same or different trained machine learning models used in block446—in some embodiments and situations, the object and optionally other attribute selection may instead be performed in block446as part of the image selection. The routine in block450further includes generating textual descriptions for each of the selected objects and other attributes, such as in accordance with any configuration settings and/or recipient information from block415, and optionally using one or more trained language models (e.g., one or more trained transformer-based machine learning models), and optionally combine the generated descriptions to generate an overall building textual description.

After block450, the routine continues to block455to, for each image group, generate a visual portion of a video using the one or more selected images of the image group that includes visual data from each of the images (e.g., in an order corresponding to a determined sequence of images), in accordance with any configuration settings and/or recipient information from block415, and such as with one or more frames for each image corresponding to one or more selected groups of visual data from that image (e.g., multiple groups of visual data from an image corresponding to one or more of panning, tilting, zooming, etc. within that image, and to include visual data corresponding to one or more selected objects or other selected attributes), and optionally further visual data to correspond to one or more transitions between visual data of different images—in other embodiments, visual data included in a generated video may include selected groups of visual data from a selected image in different locations within a video, such as with intervening visual data groups from one or more other selected images. In block460, the routine then, for each image group, generates synchronized narration for the video of that image group (e.g., for an audible portion of the video) based at least in part on the generated textual descriptions for the selected objects or other selected attributes for the image group from block450and optionally to include further descriptive information (e.g., to correspond to one or more transitions between visual data of different images, to provide an introduction and/or summary, etc.), in accordance with any configuration settings and/or recipient information from block415, and such as using one or more trained language models—in other embodiments, the narration for a video is instead generated before generation of the visual portion of the video, with the visual data included in the visual portion instead being selected to synchronize with the narration. After block460, the routine in block465then optionally provides one or more of the generated videos for presentation or otherwise presents the one or more generated videos.

If it is instead determined in block410that the instructions or other information received in block405are not to generate one or more building videos, the routine continues instead to block476to determine if the instructions or other information received in block405are to modify an existing building video. If so, the routine proceeds to block478to obtain modification instructions or other criteria related to how to perform the modification and information to indicate the video to be modified (e.g., an indication of a particular building), such as to be received in block405, to retrieve the video, and to generate a new video by modifying the retrieved video in accordance with the modification instructions or other criteria. Such modification criteria may include, for example, one or more of the following: one or more indicated time lengths (e.g., a minimum time, a maximum time, a start and end time for a subset of the video, etc.), with the retrieved video being modified accordingly (e.g., to remove segments, such as based on associated priority; to remove a beginning and/or ending portion; etc.); indications of one or more rooms (e.g., based on one or more room types), with the retrieved video being modified to exclude information about such one or more rooms; indications of one or more objects (e.g., based on one or more object types), with the retrieved video being modified to exclude information about such one or more objects; indications of one or more room groupings (e.g., a story, a multi-room apartment or condominium or town house of a larger building, a unit of a multiplex, etc.), with the retrieved video being modified to exclude information about such one or more room groupings; etc. The modification criteria may further be based in some embodiments and situations on a particular recipient, such as to personalize the modified video to that recipient, with corresponding criteria specific to the recipient being retrieved (e.g., from stored preference information for the recipient). After blocks465or478or530or590, the routine continues to block489to store the generated video(s) and optionally some or all of the other generated building information from blocks420-487and/or905-990, and optionally further provides one or more generated videos and/or at least some of the other generated building information to one or more corresponding recipients (e.g., to a user or other entity recipient from which the information and/or instructions are received in block405or that is otherwise designated in such information and/or instructions).

If it is instead determined in block476that the instructions or other information received in block405are not to modify an existing building video, the routine continues instead to block482to determine if the instructions or other information received in block405are to identify one or more generated building videos that satisfy indicated criteria (e.g., based on information about rooms and/or objects and/or other attributes described in the video, such as based at least in part on the narration accompanying the video) and/or to identify one or more target buildings having such generated building videos, and if not continues to block490. Otherwise, the routine continues to block484to retrieve candidate building videos (e.g., building videos previously generated in blocks443-478and/or905-990for one or more indicated buildings) and to compare information about such videos to the specified criteria. In block486, the routine then, for each candidate video and/or associated building, determines a degree of match of information for the candidate video/building to the criteria—if there are multiple indicated criteria, the determining of the degree of match may include combining the information for the multiple criteria in one or more manners (e.g., an average, a cumulative total, etc.). The routine further optionally rank orders the multiple candidate videos/buildings based on their degrees of match, and selects one or more best matches to use as identified target videos or buildings (e.g., all matches above a defined threshold, the single best match, etc., and optionally based on instructions or other information received in block405), with those selected one or more best matches having the highest degrees of match to the specified criteria. In block488, the routine then presents or otherwise provides information for the selected candidate video(s)/building(s) (e.g., provides one or more selected candidate videos for presentation, such as in sequence based on degree of match; provides information about one or more selected candidate buildings for presentation; etc.), such as via a building information access routine, with one example of such a routine discussed with respect toFIG.7A-7B.

If it is instead determined in block482that the information or instructions received in block405are not to identify one or more other target generated videos and/or associated buildings using one or more specified criteria, the routine continues instead to block490to perform one or more other indicated operations as appropriate. Such other operations may include, for example, receiving and responding to requests for previously generated videos and/or other building information (e.g., requests for such information for display or other presentation on one or more client devices, requests for such information to provide it to one or more other devices for use in automated navigation, etc.), training one or more neural networks or other machine learning models (e.g., classification neural networks) to determine objects and associated attributes from analysis of visual data of images and/or other acquired environmental data, training one or more neural networks (e.g., classification neural networks) or other machine learning models to determine building attributes from analysis of building floor plans (e.g., according to one or more subjective factors, such as accessibility friendly, an open floor plan, an atypical floor plan, a non-standard floor plan, etc.), training one or more machine learning models (e.g., language models) to generate attribute description information for determined objects and optionally other indicated building attributes and/or to generate building description information for a building having multiple such objects and optionally other indicated building attributes, obtaining and storing information about users of the routine (e.g., search and/or selection preferences of a current user), etc.

After blocks488or489or490, the routine continues to block495to determine whether to continue, such as until an explicit indication to terminate is received, or instead only if an explicit indication to continue is received. If it is determined to continue, the routine returns to block405to wait for additional instructions or information, and otherwise continues to block499and ends.

While not illustrated with respect to the automated operations shown in the example embodiments ofFIGS.4A-4Band in some of the automated operations ofFIGS.4C-4D, in some embodiments human users may further assist in facilitating some of the operations of the BVGUM system, such as for operator users and/or end users of the BVGUM system to provide input of one or more types that is further used in subsequent automated operations. As non-exclusive examples, such human users may provide input of one or more types as follows: to provide input to assist with the identification of objects and/or other attributes from analysis images, floor plans and/or other building information, such as to provide input in blocks441and/or442that is used as part of the automated operations for the block(s); to provide input in block446that is used as part of subsequent automated operations, such as to assist in selecting images for an image group and/or determining a sequence of selected images; to provide input in block450that is used as part of subsequent automated operations, such as to assist in selecting objects and/or other attributes to describe in a video, and/or to assist in generating textual descriptions of objects and/or other attributes, and/or to assist in generating a textual description for a building based at least in part on generated textual descriptions of objects and/or other attributes; to provide input in block455that is used as part of subsequent automated operations, such as to assist in selecting one or more groups of visual data from a selected image, and/or to assist in specifying transitions to use between images; to provide input in block460that is used as part of subsequent automated operations, such as to assist in determining a narration for a video based on generated textual descriptions, and/or to produce an audible version of the narration; etc. Additional details are included elsewhere herein regarding embodiments in which human user(s) provide input used in additional automated operations of the BVGUM system.

FIG.5illustrates an example flow diagram of an embodiment of an ICA (Image Capture & Analysis) system routine500. The routine may be performed by, for example, the ICA system160ofFIG.1, the ICA system388ofFIG.3, and/or an ICA system as described with respect toFIGS.2A-2Vand elsewhere herein, such as to acquire 360° panorama images and/or other images at acquisition locations within buildings or other structures, such as for use in subsequent generation of related floor plans and/or other mapping information. While portions of the example routine500are discussed with respect to acquiring particular types of images at particular acquisition locations, it will be appreciated that this or a similar routine may be used to acquire video (with video frame images) and/or other data (e.g., audio), whether instead of or in addition to such panorama images or other perspective images. In addition, while the illustrated embodiment acquires and uses information from the interior of a target building, it will be appreciated that other embodiments may perform similar techniques for other types of data, including for non-building structures and/or for information external to one or more target buildings of interest (e.g., on a property on which a target building is located, such as to show yards, decks, patios, accessory structures, etc.). Furthermore, some or all of the routine may be executed on a mobile device used by a user to acquire image information, and/or by a system remote from such a mobile device. In at least some embodiments, the routine500may be invoked from block430of routine400ofFIGS.4A-4D, with corresponding information from routine500provided to routine400as part of implementation of that block430, and with processing control returned to routine400after blocks577and/or599in such situations—in other embodiments, the routine400may proceed with additional operations in an asynchronous manner without waiting for such processing control to be returned (e.g., to proceed with other processing activities while waiting for the corresponding information from the routine500to be provided to routine400).

The illustrated embodiment of the routine begins at block505, where instructions or information are received. At block510, the routine determines whether the received instructions or information indicate to acquire visual data and/or other data representing a building interior (optionally in accordance with supplied information about one or more additional acquisition locations and/or other guidance acquisition instructions), and if not continues to block590. Otherwise, the routine proceeds to block512to receive an indication to begin the image acquisition process at a first acquisition location (e.g., from a user of a mobile image acquisition device that will perform the acquisition process). After block512, the routine proceeds to block515in order to perform acquisition location image acquisition activities for acquiring a 360° panorama image for the acquisition location in the interior of the target building of interest, such as via one or more fisheye lenses and/or non-fisheye rectilinear lenses on the mobile device and to provide horizontal coverage of at least 360° around a vertical axis, although in other embodiments other types of images and/or other types of data may be acquired. As one non-exclusive example, the mobile image acquisition device may be a rotating (scanning) panorama camera equipped with a fisheye lens (e.g., with 180° of horizontal coverage) and/or other lens (e.g., with less than 180° of horizontal coverage, such as a regular lens or wide-angle lens or ultrawide lens). The routine may also optionally obtain annotation and/or other information from the user regarding the acquisition location and/or the surrounding environment, such as for later use in presentation of information regarding that acquisition location and/or surrounding environment.

After block515is completed, the routine continues to block520to determine if there are more acquisition locations at which to acquire images, such as based on corresponding information provided by the user of the mobile device and/or received in block505—in some embodiments, the ICA routine will acquire only a single image and then proceed to block577to provide that image and corresponding information (e.g., to return the image and corresponding information to the BVGUM system and/or MIGM system for further use before receiving additional instructions or information to acquire one or more next images at one or more next acquisition locations). If there are more acquisition locations at which to acquire additional images at the current time, the routine continues to block522to optionally initiate the capture of linking information (e.g., acceleration data) during movement of the mobile device along a travel path away from the current acquisition location and towards a next acquisition location within the building interior. The captured linking information may include additional sensor data (e.g., from one or more IMU, or inertial measurement units, on the mobile device or otherwise carried by the user) and/or additional visual information (e.g., images, video, etc.) recorded during such movement. Initiating the capture of such linking information may be performed in response to an explicit indication from a user of the mobile device or based on one or more automated analyses of information recorded from the mobile device. In addition, the routine may further optionally monitor the motion of the mobile device in some embodiments during movement to the next acquisition location, and provide one or more guidance cues (e.g., to the user) regarding the motion of the mobile device, quality of the sensor data and/or visual information being captured, associated lighting/environmental conditions, advisability of capturing a next acquisition location, and any other suitable aspects of capturing the linking information. Similarly, the routine may optionally obtain annotation and/or other information from the user regarding the travel path, such as for later use in presentation of information regarding that travel path or a resulting inter-panorama image connection link. In block524, the routine determines that the mobile device has arrived at the next acquisition location (e.g., based on an indication from the user, based on forward movement of the mobile device stopping for at least a predefined amount of time, etc.), for use as the new current acquisition location, and returns to block515to perform the acquisition location image acquisition activities for the new current acquisition location.

If it is instead determined in block520that there are not any more acquisition locations at which to acquire image information for the current building or other structure at the current time, the routine proceeds to block545to optionally preprocess the acquired 360° panorama images before their subsequent use (e.g., for generating related mapping information, for providing information about structural elements or other objects of rooms or other enclosing areas, etc.), such as to produce images of a particular type and/or in a particular format (e.g., to perform an equirectangular projection for each such image, with straight vertical data such as the sides of a typical rectangular door frame or a typical border between 2 adjacent walls remaining straight, and with straight horizontal data such as the top of a typical rectangular door frame or a border between a wall and a floor remaining straight at a horizontal midline of the image but being increasingly curved in the equirectangular projection image in a convex manner relative to the horizontal midline as the distance increases in the image from the horizontal midline). In block577, the images and any associated generated or obtained information is stored for later use, and optionally provided to one or more recipients (e.g., to block430of routine400if invoked from that block).FIGS.6A-6Billustrate one example of a routine for generating a floor plan representation of a building interior from the generated panorama information.

If it is instead determined in block510that the instructions or other information received in block505are not to acquire images and other data representing a building interior, the routine continues instead to block590to perform any other indicated operations as appropriate, such as to configure parameters to be used in various operations of the system (e.g., based at least in part on information specified by a user of the system, such as a user of a mobile device who captures one or more building interiors, an operator user of the ICA system, etc.), to respond to requests for generated and stored information (e.g., to identify one or more groups of inter-connected linked panorama images each representing a building or part of a building that match one or more specified search criteria, one or more panorama images that match one or more specified search criteria, etc.), to generate and store inter-panorama image connections between panorama images for a building or other structure (e.g., for each panorama image, to determine directions within that panorama image toward one or more other acquisition locations of one or more other panorama images, such as to enable later display of an arrow or other visual representation with a panorama image for each such determined direction from the panorama image to enable an end-user to select one of the displayed visual representations to switch to a display of the other panorama image at the other acquisition location to which the selected visual representation corresponds), to obtain and store other information about users of the system, to perform any housekeeping tasks, etc.

Following blocks577or590, the routine proceeds to block595to determine whether to continue, such as until an explicit indication to terminate is received, or instead only if an explicit indication to continue is received. If it is determined to continue, the routine returns to block505to await additional instructions or information, and if not proceeds to step599and ends.

While not illustrated with respect to the automated operations shown in the example embodiment ofFIG.5, in some embodiments human users may further assist in facilitating some of the operations of the ICA system, such as for operator users and/or end users of the ICA system to provide input of one or more types that is further used in subsequent automated operations. As non-exclusive examples, such human users may provide input of one or more types as follows: to provide input to assist with determination of acquisition locations, such as to provide input in blocks512and/or524that is used as part of the automated operations for that block; to perform activities in block515related to image acquisition (e.g., to participate in the image acquisition, such as to activate the shutter, implement settings on a camera and/or associated sensor or component, rotate a camera as part of capturing a panorama image, etc.; to set the location and/or orientation of one or more camera devices and/or associated sensors or components; etc.); to provide input in blocks515and/or522that is used as part of subsequent automated operations, such as labels, annotations or other descriptive information with respect to particular images, surrounding rooms and/or objects in the rooms; etc. Additional details are included elsewhere herein regarding embodiments in which one or more human users provide input that is further used in additional automated operations of the ICA system.

FIGS.6A-6Billustrate an example embodiment of a flow diagram for a MIGM (Mapping Information Generation Manager) system routine600. The routine may be performed by, for example, execution of the MIGM system160ofFIG.1, the MIGM system389ofFIG.3, and/or a MIGM system as described with respect toFIGS.2A-2Vand elsewhere herein, such as to determine a room shape for a room (or other defined area) by analyzing information from one or more images acquired in the room (e.g., one or more 360° panorama images), to generate a partial or complete floor plan for a building or other defined area based at least in part on one or more images of the area and optionally additional data captured by a mobile computing device and using determined room shapes, and/or to generate other mapping information for a building or other defined area based at least in part on one or more images of the area and optionally additional data captured by a mobile computing device. In the example ofFIGS.6A-6B, the determined room shape for a room may be a 2D room shape to represent the locations of the walls of the room or a 3D fully closed combination of planar surfaces to represent the locations of walls and ceiling and floor of the room, and the generated mapping information for a building (e.g., a house) may include a 2D floor plan and/or 3D computer model floor plan, but in other embodiments, other types of room shapes and/or mapping information may be generated and used in other manners, including for other types of structures and defined areas, as discussed elsewhere herein. In at least some embodiments, the routine600may be invoked from block440of routine400ofFIGS.4A-4D, with corresponding information from routine600provided to routine400as part of implementation of that block440, and with processing control returned to routine400after blocks688and/or699in such situations—in other embodiments, the routine400may proceed with additional operations in an asynchronous manner without waiting for such processing control to be returned (e.g., to proceed to block445once the corresponding information from routine600is provided to routine400, to proceed with other processing activities while waiting for the corresponding information from the routine600to be provided to routine400, etc.).

The illustrated embodiment of the routine begins at block605, where information or instructions are received. The routine continues to block610to determine whether image information is already available to be analyzed for one or more rooms (e.g., for some or all of an indicated building, such as based on one or more such images received in block605as previously generated by the ICA routine), or if such image information instead is to be currently acquired. If it is determined in block610to currently acquire some or all of the image information, the routine continues to block612to acquire such information, optionally waiting for one or more users or devices to move throughout one or more rooms of a building and acquire panoramas or other images at one or more acquisition locations in one or more of the rooms (e.g., at multiple acquisition locations in each room of the building), optionally along with metadata information regarding the acquisition and/or interconnection information related to movement between acquisition locations, as discussed in greater detail elsewhere herein—implementation of block612may, for example, include invoking an ICA system routine to perform such activities, withFIG.5providing one example embodiment of an ICA system routine for performing such image acquisition. If it is instead determined in block610not to currently acquire the images, the routine continues instead to block615to obtain one or more existing panoramas or other images from one or more acquisition locations in one or more rooms (e.g., multiple images acquired at multiple acquisition locations that include at least one image and acquisition location in each room of a building), optionally along with metadata information regarding the acquisition and/or interconnection information related to movement between the acquisition locations, such as may in some situations have been supplied in block605along with the corresponding instructions.

After blocks612or615, the routine continues to block620, where it determines whether to generate mapping information that includes an inter-linked set of target panorama images (or other images) for a building or other group of rooms (referred to at times as a ‘virtual tour’, such as to enable an end user to move from any one of the images of the linked set to one or more other images to which that starting current image is linked, including in some embodiments via selection of a user-selectable control for each such other linked image that is displayed along with a current image, optionally by overlaying visual representations of such user-selectable controls and corresponding inter-image directions on the visual data of the current image, and to similarly move from that next image to one or more additional images to which that next image is linked, etc.), and if so continues to block625. The routine in block625selects pairs of at least some of the images (e.g., based on the images of a pair having overlapping visual content), and determines, for each pair, relative directions between the images of the pair based on shared visual content and/or on other captured linking interconnection information (e.g., movement information) related to the images of the pair (whether movement directly from the acquisition location for one image of a pair to the acquisition location of another image of the pair, or instead movement between those starting and ending acquisition locations via one or more other intermediary acquisition locations of other images). The routine in block625may further optionally use at least the relative direction information for the pairs of images to determine global relative positions of some or all of the images to each other in a common coordinate system, and/or generate the inter-image links and corresponding user-selectable controls as noted above. Additional details are included elsewhere herein regarding creating such a linked set of images.

After block625, or if it is instead determined in block620that the instructions or other information received in block605are not to determine a linked set of images, the routine continues to block635to determine whether the instructions received in block605indicate to generate other mapping information for an indicated building (e.g., a floor plan), and if so the routine continues to perform some or all of blocks637-685to do so, and otherwise continues to block690. In block637, the routine optionally obtains additional information about the building, such as from activities performed during acquisition and optionally analysis of the images, and/or from one or more external sources (e.g., online databases, information provided by one or more end users, etc.)—such additional information may include, for example, exterior dimensions and/or shape of the building, additional images and/or annotation information acquired corresponding to particular locations external to the building (e.g., surrounding the building and/or for other structures on the same property, from one or more overhead locations, etc.), additional images and/or annotation information acquired corresponding to particular locations within the building (optionally for locations different from acquisition locations of the acquired panorama images or other images), etc.

After block637, the routine continues to block640to select the next room (beginning with the first) for which one or more images (e.g., 360° panorama images) acquired in the room are available, and to analyze the visual data of the image(s) for the room to determine a room shape (e.g., by determining at least wall locations), optionally along with determining uncertainty information about walls and/or other parts of the room shape, and optionally including identifying other wall and floor and ceiling elements (e.g., wall structural elements/objects, such as windows, doorways and stairways and other inter-room wall openings and connecting passages, wall borders between a wall and another wall and/or ceiling and/or floor, etc.) and their positions within the determined room shape of the room. In some embodiments, the room shape determination may include using boundaries of the walls with each other and at least one of the floor or ceiling to determine a 2D room shape (e.g., using one or trained machine learning models), while in other embodiments the room shape determination may be performed in other manners (e.g., by generating a 3D point cloud of some or all of the room walls and optionally the ceiling and/or floor, such as by analyzing at least visual data of the panorama image and optionally additional data captured by an image acquisition device or associated mobile computing device, optionally using one or more of SfM (Structure from Motion) or SLAM (Simultaneous Location And Mapping) or MVS (Multi-View Stereo) analysis). In addition, the activities of block645may further optionally determine and use initial pose information for each of those panorama images (e.g., as supplied with acquisition metadata for the panorama image), and/or obtain and use additional metadata for each panorama image (e.g., acquisition height information of the camera device or other image acquisition device used to acquire a panorama image relative to the floor and/or the ceiling). Additional details are included elsewhere herein regarding determining room shapes and identifying additional information for the rooms. After block640, the routine continues to block645, where it determines whether there are more rooms for which to determine room shapes based on images acquired in those rooms, and if so returns to block640to select the next such room for which to determine a room shape.

If it is instead determined in block645that there are not more rooms for which to generate room shapes, the routine continues to block660to determine whether to further generate at least a partial floor plan for the building (e.g., based at least in part on the determined room shape(s) from block640, and optionally further information regarding how to position the determined room shapes relative to each other). If not, such as when determining only one or more room shapes without generating further mapping information for a building (e.g., to determine the room shape for a single room based on one or more images acquired in the room by the ICA system), the routine continues to block688. Otherwise, the routine continues to block665to retrieve one or more room shapes (e.g., room shapes generated in block645) or otherwise obtain one or more room shapes (e.g., based on human-supplied input) for rooms of the building, whether 2D or 3D room shapes, and then continues to block670. In block670, the routine uses the one or more room shapes to create an initial floor plan (e.g., an initial 2D floor plan using 2D room shapes and/or an initial 3D floor plan using 3D room shapes), such as a partial floor plan that includes one or more room shapes but less than all room shapes for the building, or a complete floor plan that includes all room shapes for the building. If there are multiple room shapes, the routine in block670further determines positioning of the room shapes relative to each other, such as by using visual overlap between images from multiple acquisition locations to determine relative positions of those acquisition locations and of the room shapes surrounding those acquisition locations, and/or by using other types of information (e.g., using connecting inter-room passages between rooms, optionally applying one or more constraints or optimizations, etc.). In at least some embodiments, the routine in block670further refines some or all of the room shapes by generating a binary segmentation mask that covers the relatively positioned room shape(s), extracting a polygon representing the outline or contour of the segmentation mask, and separating the polygon into the refined room shape(s). Such a floor plan may include, for example, relative position and shape information for the various rooms without providing any actual dimension information for the individual rooms or building as a whole, and may further include multiple linked or associated sub-maps (e.g., to reflect different stories, levels, sections, etc.) of the building. The routine further optionally associates positions of the doors, wall openings and other identified wall elements on the floor plan.

After block670, the routine optionally performs one or more steps680-685to determine and associate additional information with the floor plan. In block680, the routine optionally estimates the dimensions of some or all of the rooms, such as from analysis of images and/or their acquisition metadata or from overall dimension information obtained for the exterior of the building, and associates the estimated dimensions with the floor plan—it will be appreciated that if sufficiently detailed dimension information were available, architectural drawings, blueprints, etc. may be generated from the floor plan. After block680, the routine continues to block683to optionally associate further information with the floor plan (e.g., with particular rooms or other locations within the building), such as additional existing images with specified positions and/or annotation information. In block685, if the room shapes from block645are not 3D room shapes, the routine further optionally estimates heights of walls in some or all rooms, such as from analysis of images and optionally sizes of known objects in the images, as well as height information about a camera when the images were acquired, and uses that height information to generate 3D room shapes for the rooms. The routine further optionally uses the 3D room shapes (whether from block640or block685) to generate a 3D computer model floor plan of the building, with the 2D and 3D floor plans being associated with each other—in other embodiments, only a 3D computer model floor plan may be generated and used (including to provide a visual representation of a 2D floor plan if so desired by using a horizontal slice of the 3D computer model floor plan).

After block685, or if it is instead determined in block660not to determine a floor plan, the routine continues to block688to store the determined room shape(s) and/or generated mapping information and/or other generated information, to optionally provide some or all of that information to one or more recipients (e.g., to block440of routine400if invoked from that block), and to optionally further use some or all of the determined and generated information, such as to provide the generated 2D floor plan and/or 3D computer model floor plan for display on one or more client devices and/or to one or more other devices for use in automating navigation of those devices and/or associated vehicles or other entities, to similarly provide and use information about determined room shapes and/or a linked set of images and/or about additional information determined about contents of rooms and/or passages between rooms, etc.

If it is instead determined in block635that the information or instructions received in block605are not to generate mapping information for an indicated building, the routine continues instead to block690to perform one or more other indicated operations as appropriate. Such other operations may include, for example, receiving and responding to requests for previously generated floor plans and/or previously determined room shapes and/or other generated information (e.g., requests for such information for display on one or more client devices, requests for such information to provide it to one or more other devices for use in automated navigation, etc.), obtaining and storing information about buildings for use in later operations (e.g., information about dimensions, numbers or types of rooms, total square footage, adjacent or nearby other buildings, adjacent or nearby vegetation, exterior images, etc.), etc.

After blocks688or690, the routine continues to block695to determine whether to continue, such as until an explicit indication to terminate is received, or instead only if an explicit indication to continue is received. If it is determined to continue, the routine returns to block605to wait for and receive additional instructions or information, and otherwise continues to block699and ends.

While not illustrated with respect to the automated operations shown in the example embodiment ofFIGS.6A-6B, in some embodiments human users may further assist in facilitating some of the operations of the MIGM system, such as for operator users and/or end users of the MIGM system to provide input of one or more types that is further used in subsequent automated operations. As non-exclusive examples, such human users may provide input of one or more types as follows: to provide input to assist with the linking of a set of images, such as to provide input in block625that is used as part of the automated operations for that block (e.g., to specify or adjust initial automatically determined directions between one or more pairs of images, to specify or adjust initial automatically determined final global positions of some or all of the images relative to each other, etc.); to provide input in block637that is used as part of subsequent automated operations, such as one or more of the illustrated types of information about the building; to provide input with respect to block640that is used as part of subsequent automated operations, such as to specify or adjust initial automatically determined element locations and/or estimated room shapes and/or to manually combine information from multiple estimated room shapes for a room (e.g., separate room shape estimates from different images acquired in the room) to create a final room shape for the room and/or to specify or adjust initial automatically determined information about a final room shape, etc.; to provide input with respect to block670, that is used as part of subsequent operations, such as to specify or adjust initial automatically determined positions of room shapes within a floor plan being generated and/or to specify or adjust initial automatically determined room shapes themselves within such a floor plan; to provide input with respect to one or more of blocks680and683and685that is used as part of subsequent operations, such as to specify or adjust initial automatically determined information of one or more types discussed with respect to those blocks; and/or to specify or adjust initial automatically determined pose information (whether initial pose information or subsequent updated pose information) for one or more of the panorama images; etc. Additional details are included elsewhere herein regarding embodiments in which human user(s) provide input that is further used in additional automated operations of the MIGM system.

FIG.7A-7Billustrate an example embodiment of a flow diagram for a Building Information Access system routine700. The routine may be performed by, for example, execution of a building information access client computing device175and its software system(s) (not shown) ofFIG.1, a client computing device390ofFIG.3, and/or a building information access viewer or presentation system as described elsewhere herein, such as to request and receive and present building information (e.g., videos; individual images; floor plans and/or other mapping-related information, such as determined room structural layouts/shapes, a virtual tour of inter-linked images, etc.; generated building description information; etc.), to provide user input related to videos to be generated, to obtain and display information about images matching one or more indicated target images, to obtain and display guidance acquisition instructions (e.g., with respect to other images acquired during an acquisition session and/or for an associated building, such as part of a displayed GUI), etc. In the example ofFIG.7A-7B, the presented information is for one or more buildings (such as an interior of a house), but in other embodiments, other types of mapping information may be presented for other types of buildings or environments and used in other manners, as discussed elsewhere herein.

The illustrated embodiment of the routine begins at block705, where instructions or information are received. At block707, the routine determines whether the received instructions or information in block705are to obtain user input related to generating one or more videos for an indicated building, and if so continues to perform blocks805-840. Otherwise, the routine continues to block710to determine whether the received instructions or information in block705are to present determined information for one or more target buildings, and if so continues to block715to determine whether the received instructions or information in block705are to select one or more target buildings using specified criteria (e.g., based at least in part on an indicated building), and if not continues to block720to obtain an indication of a target building to use from the user (e.g., based on a current user selection, such as from a displayed list or other user selection mechanism; based on information received in block705; etc.). Otherwise, if it is determined in block715to select one or more target buildings from specified criteria, the routine continues instead to block725, where it obtains indications of one or more search criteria to use, such as from current user selections or as indicated in the information or instructions received in block705, and then searches stored information about buildings (e.g., floor plans, videos, generated textual descriptions, etc.) to determine one or more of the buildings that satisfy the search criteria or otherwise obtains indications of one or more such matching target buildings, such as information that is currently or previously generated by the BVGUM system (with one example of operations of such a system being further discussed with respect toFIGS.4A-4D, and with the BVGUM system optionally invoked in block720to obtain such information). In the illustrated embodiment, the routine then further optionally selects a best match target building from the one or more determined target buildings (e.g., the target building with the highest similarity or other matching rating for the specified criteria, or using another selection technique indicated in the instructions or other information received in block705), while in other embodiments the routine may instead present information for multiple target buildings that satisfy the search criteria (e.g., in a ranked order based on degree of match; in a sequential manner, such as to present one or more videos for each of multiple buildings in a sequence; etc.) and receive a user selection of the best match target building from the multiple candidate target buildings.

After blocks720or725, the routine continues to block730to determine whether the instructions or other information received in block705indicate to present one or more generated videos for each of one or more target buildings, and if so continues to block732to do so, including to retrieve one or more existing generated videos for each target building (e.g., one or more existing generated videos that match criteria specified in the information of block705or otherwise determined, such as using preference information or other information specific to a recipient), or alternatively in some embodiments and situations to request a dynamically generated video (e.g., by interacting with the BVGUM system to cause such generation, whether by newly generating a video or by modifying an existing video, and optionally supplying one or more criteria to use in such generation, such as using preference information or other information specific to a recipient), and to initiate presentation of the retrieved and/or dynamically generated video(s) (e.g., to transmit video(s) to client device(s) for presentation on those devices). After block732, the routine continues to block795.

If it is instead determined in block730that the instructions or other information received in block705do not indicate to present one or more generated videos, the routine continues to block735to retrieve information for the target building for display (e.g., a floor plan; other generated mapping information for the building, such as a group of inter-linked images for use as part of a virtual tour; generated building description information; etc.), and optionally indications of associated linked information for the building interior and/or a surrounding location external to the building, and/or information about one or more generated explanations or other descriptions of the target building, and selects an initial view of the retrieved information (e.g., a view of the floor plan, a particular room shape, a particular image, some or all of the generated building description information, etc.). In block740, the routine then displays or otherwise presents the current view of the retrieved information, and waits in block745for a user selection. After a user selection in block745, if it is determined in block750that the user selection corresponds to adjusting the current view for the current target building (e.g., to change one or more aspects of the current view), the routine continues to block755to update the current view in accordance with the user selection, and then returns to block740to update the displayed or otherwise presented information accordingly. The user selection and corresponding updating of the current view may include, for example, displaying or otherwise presenting a piece of associated linked information that the user selects (e.g., a particular image associated with a displayed visual indication of a determined acquisition location, such as to overlay the associated linked information over at least some of the previous display; a particular other image linked to a current image and selected from the current image using a user-selectable control overlaid on the current image to represent that other image; etc.), and/or changing how the current view is displayed (e.g., zooming in or out; rotating information if appropriate; selecting a new portion of the floor plan to be displayed or otherwise presented, such as with some or all of the new portion not being previously visible, or instead with the new portion being a subset of the previously visible information; etc.). If it is instead determined in block750that the user selection is not to display further information for the current target building (e.g., to display information for another building, to end the current display operations, etc.), the routine continues instead to block795, and returns to block705to perform operations for the user selection if the user selection involves such further operations.

If it is instead determined in block710that the instructions or other information received in block705are not to present information representing a building, the routine continues instead to block760to determine whether the instructions or other information received in block705indicate to identify other images (if any) corresponding to one or more indicated target images, and if so continues to blocks765-770to perform such activities. In particular, the routine in block765receives the indications of the one or more target images for the matching (such as from information received in block705or based on one or more current interactions with a user) along with one or more matching criteria (e.g., an amount of visual overlap), and in block770identifies one or more other images (if any) that match the indicated target image(s), such as by interacting with the ICA and/or MIGM systems to obtain the other image(s). The routine then displays or otherwise provides information in block770about the identified other image(s), such as to provide information about them as part of search results, to display one or more of the identified other image(s), etc. If it is instead determined in block760that the instructions or other information received in block705are not to identify other images corresponding to one or more indicated target images, the routine continues instead to block775to determine whether the instructions or other information received in block705correspond to obtaining and providing guidance acquisition instructions during an image acquisition session with respect to one or more indicated target images (e.g., a most recently acquired image), and if so continues to block780, and otherwise continues to block790. In block780, the routine obtains information about guidance acquisition instructions of one or more types, such as by interacting with the ICA system, and displays or otherwise provides information in block780about the guidance acquisition instructions, such as by overlaying the guidance acquisition instructions on a partial floor plan and/or recently acquired image in manners discussed in greater detail elsewhere herein.

If it is determined in block707that the received instructions or information in block705are to obtain user input related to generating one or more videos for an indicated building, the routine continues to perform blocks805-840to do so. In particular, the routine in block805receives an indication of the building, such as based on input provided in block705or based on information specified in block805by one or more users via a GUI presented to the user(s), such as to select the building from a list and/or from search results. In block810, the routine then retrieves information about the building that includes a floor plan for the building and information about building attributes of the building, and optionally additional building information (e.g., one or more existing videos for the building, images acquired at the building, etc.), and in block815, if there are any existing videos and the retrieved information does not include a visual representation for such an existing video (e.g., of an associated path through the building that the camera capturing that video travels while capturing the visual data of that video), generates one or more such visual representations for each such video. In block820, the routine then presents information about the building to one or more users in a displayed GUI, such as a visual representation of the floor plan overlaid with visual representations of existing videos (if any), and optionally information about building attributes and building images shown at associated positions on the floor plan or otherwise provided in association with the floor plan (e.g., to provide a list or other group of building images associated with a room or other area of the building, to provide a list or other group of building attributes associated with a room or other area of the building, etc.). The routine further provides one or more user-selectable controls to enable the user(s) to provide one or more segment criteria for use in generating one or more additional videos from one or more existing videos and/or to provide one or more generation criteria for use in generating one or more new videos based at least in part on visual data of at least some of the building images—the user input may include, for example, indicating one or more portions of one or more existing videos to use in generating a corresponding additional video and/or specifying types of information to include a new video to be generated (e.g., by selecting a portion of a presented visual representation of an existing video, by specifying one or more rooms or other areas for which to include visual data in a generated additional video from existing video(s) or generated new video from images, by specifying one or more building attributes for which to include visual data in a generated additional video from existing video(s) or generated new video from images, etc.). In block825, the routine then receives user input using the one or more user-selectable controls to specify one or more segment criteria and/or one or more generation criteria to use in generating one or more additional videos and/or new videos, respectively, and in block430provides the user input to a BVGUM system routine to cause the generation of the one or more additional videos and/or new videos based on the specified segment criteria and/or generation criteria, respectively, and with one example of such a routine described with respect toFIGS.4A-4D. In block840, the routine then receives the one or more generated videos, and optionally presents to the user(s) the videos and/or information about the generated videos (e.g., by updating the presentation of the floor plan in the GUI, such as to add visual representations of a path for each such generated video and/or other information about each such generated video).

In block790, the routine continues instead to perform other indicated operations as appropriate, such as to configure parameters to be used in various operations of the system (e.g., based at least in part on information specified by a user of the system, such as a user of a mobile device who acquires one or more building interiors, an operator user of the BVGUM and/or MIGM systems, etc., including for use in personalizing information display for a particular recipient user in accordance with his/her preferences or other information specific to that recipient), to obtain and store other information about users of the system (e.g., preferences or other information specific to that user), to respond to requests for generated and stored information, to perform any housekeeping tasks, etc.

Following blocks732or770or780or790or840, or if it is determined in block750that the user selection does not correspond to the current building, the routine proceeds to block795to determine whether to continue, such as until an explicit indication to terminate is received, or instead only if an explicit indication to continue is received. If it is determined to continue (including if the user made a selection in block745related to a new building to present), the routine returns to block705to await additional instructions or information (or to continue directly on to block735if the user made a selection in block745related to a new building to present), and if not proceeds to step799and ends.

Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It will be appreciated that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. It will be further appreciated that in some implementations the functionality provided by the routines discussed above may be provided in alternative ways, such as being split among more routines or consolidated into fewer routines. Similarly, in some implementations illustrated routines may provide more or less functionality than is described, such as when other illustrated routines instead lack or include such functionality respectively, or when the amount of functionality that is provided is altered. In addition, while various operations may be illustrated as being performed in a particular manner (e.g., in serial or in parallel, or synchronous or asynchronous) and/or in a particular order, in other implementations the operations may be performed in other orders and in other manners. Any data structures discussed above may also be structured in different manners, such as by having a single data structure split into multiple data structures and/or by having multiple data structures consolidated into a single data structure. Similarly, in some implementations illustrated data structures may store more or less information than is described, such as when other illustrated data structures instead lack or include such information respectively, or when the amount or types of information that is stored is altered.

From the foregoing it will be appreciated that, although specific embodiments have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the invention. Accordingly, the invention is not limited except as by corresponding claims and the elements recited by those claims. In addition, while certain aspects of the invention may be presented in certain claim forms at certain times, the inventors contemplate the various aspects of the invention in any available claim form. For example, while only some aspects of the invention may be recited as being embodied in a computer-readable medium at particular times, other aspects may likewise be so embodied.