Patent ID: 12236526

In accordance with common practice the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may not depict all of the components of a given system, method or device. Finally, like reference numerals may be used to denote like features throughout the specification and figures.

DESCRIPTION

Numerous details are described in order to provide a thorough understanding of the example implementations shown in the drawings. However, the drawings merely show some example aspects of the present disclosure and are therefore not to be considered limiting. Those of ordinary skill in the art will appreciate that other effective aspects and/or variants do not include all of the specific details described herein. Moreover, well-known systems, methods, components, devices and circuits have not been described in exhaustive detail so as not to obscure more pertinent aspects of the example implementations described herein.

FIG.1is a block diagram of an example operating environment100in accordance with some implementations. In this example, the example operating environment100illustrates an example physical environment105that includes walls130,132,134, chair140, table142, door150. Additionally, the example operating environment100includes a person (e.g., user102) holding a device (e.g., device120), a person (e.g., movable object104) sitting on the chair140, and a person (e.g., movable object106) standing in the physical environment105. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment100includes a server110and a device120. In an exemplary implementation, the operating environment100does not include a server110, and the methods described herein are performed on the device120.

In some implementations, the server110is configured to manage and coordinate an experience for the user. In some implementations, the server110includes a suitable combination of software, firmware, and/or hardware. The server110is described in greater detail below with respect toFIG.2. In some implementations, the server110is a computing device that is local or remote relative to the physical environment105. In one example, the server110is a local server located within the physical environment105. In another example, the server110is a remote server located outside of the physical environment105(e.g., a cloud server, central server, etc.). In some implementations, the server110is communicatively coupled with the device120via one or more wired or wireless communication channels (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x, etc.).

In some implementations, the device120is configured to present an environment to the user. In some implementations, the device120includes a suitable combination of software, firmware, and/or hardware. The device120is described in greater detail below with respect toFIG.3. In some implementations, the functionalities of the server110are provided by and/or combined with the device120.

In some implementations, the device120is a handheld electronic device (e.g., a smartphone or a tablet) configured to present content to the user. In some implementations, the user102wears the device120on his/her head. As such, the device120may include one or more displays provided to display content. For example, the device120may enclose the field-of-view of the user102. In some implementations, the device120is replaced with a chamber, enclosure, or room configured to present content in which the user102does not wear or hold the device120.

FIG.2is a block diagram of an example of the server110in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations the server110includes one or more processing units202(e.g., microprocessors, application-specific integrated-circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), central processing units (CPUs), processing cores, and/or the like), one or more input/output (I/O) devices206, one or more communication interfaces208(e.g., universal serial bus (USB), FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, global system for mobile communications (GSM), code division multiple access (CDMA), time division multiple access (TDMA), global positioning system (GPS), infrared (IR), BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces210, a memory220, and one or more communication buses204for interconnecting these and various other components.

In some implementations, the one or more communication buses204include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices206include at least one of a keyboard, a mouse, a touchpad, a joystick, one or more microphones, one or more speakers, one or more image sensors, one or more displays, and/or the like.

The memory220includes high-speed random-access memory, such as dynamic random-access memory (DRAM), static random-access memory (SRAM), double-data-rate random-access memory (DDR RAM), or other random-access solid-state memory devices. In some implementations, the memory220includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory220optionally includes one or more storage devices remotely located from the one or more processing units202. The memory220comprises a non-transitory computer readable storage medium. In some implementations, the memory220or the non-transitory computer readable storage medium of the memory220stores the following programs, modules and data structures, or a subset thereof including an optional operating system230and one or more applications240.

The operating system230includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the applications240are configured to manage and coordinate one or more experiences for one or more users (e.g., a single experience for one or more users, or multiple experiences for respective groups of one or more users).

The applications240include an integration unit242, a segmentation unit244, a mask data unit246, and a 3D model unit248. The integration unit242, the segmentation unit244, the mask data unit246, and the 3D model unit248can be combined into a single application or unit or separated into one or more additional applications or units.

The integration unit242is configured with instructions executable by a processor to obtain image data (e.g., light intensity data, depth data, camera position information, etc.) and integrate (e.g., fuse) the image data using one or more of the techniques disclosed herein. For example, the integration unit242fuses RGB images from a light intensity camera with a sparse depth map from a depth camera (e.g., time-of-flight sensor) and other sources of physical environment information (e.g., camera positioning information from a camera's SLAM system, VIO, or the like) to output a dense depth point cloud of information.

The segmentation unit244is configured with instructions executable by a processor to generate segmentation data of the physical environment using one or more of the techniques disclosed herein. For example, the segmentation unit244obtains a sequence of light intensity images (e.g., RGB) from a light intensity camera (e.g., a live camera feed) and performs a semantic segmentation algorithm to assign semantic labels to recognized features (e.g., walls, doors, floor, windows, etc.) and/or objects (e.g., furniture, appliances, people, etc.) in the image data. The segmentation unit244can then generate segmented data, i.e., semantic image data (e.g., RGB-S), using one or more of the techniques disclosed herein. In some implementations, the segmentation includes confidence levels for each identified feature and/or object for each pixel location.

The mask data unit246is configured with instructions executable by a processor to generate a mask (e.g., a segmentation mask) identifying portions of an image associated with an object (e.g., an object associated with movement) based on the image data using one or more techniques disclosed herein. For example, the mask data unit246obtains depth data from the integration unit610, the segmentation data from the segmentation unit244, and generates a segmentation mask for all identified movable objects (e.g., identifies all locations of identified people for the 3D data to provide to the 3D model unit248). Alternatively, the mask data unit246generates a segmentation mask based on the segmentation data from the segmentation unit244. Alternatively, the mask data unit246generates a segmentation mask based directly on the obtained depth data (e.g., a depth camera on the device120).

In some implementations, the mask data unit246includes a plurality of machine learning units for each specific type of object associated with movement. For example, a class-1 neural network for people, a class-2 neural network for cats, a class-3 neural network for dogs, etc. The plurality of machine learning units can be trained for a different subset of objects such that the mask data unit246can provide accurate masks for each subset of object associated with movement. Alternatively, the mask data can be used to mask (e.g., filter, remove, exclude, etc.) any type of object or feature that is desired to not be included in the 3D model such that the mask data unit246includes a plurality of machine learning units for each specific type of object. Additionally, or alternatively, the mask data unit246can include a machine learning unit trained to identify an object that is moving and creating distortion or noise in the acquired image data and determine to generate a mask for that particular object (e.g., a mechanical device in the room that is generating large movements such as a large ceiling fan).

The 3D model unit248is configured with instructions executable by a processor to obtain 3D data, segmentation data, and mask data, and generate a 3D model using one or more techniques disclosed herein. For example, the 3D model unit248obtains 3D data (e.g., 3D point cloud data) from the integration unit242, obtains segmentation data (e.g., RGB-S data) from the segmentation unit244, obtains mask data from the mask data unit246, other sources of physical environment information (e.g., camera positioning information), and generates a 3D model (e.g., a 3D mesh representation, a 3D point cloud with associated semantic labels, or the like) that excludes objects or features identified by the mask data.

Although these elements are shown as residing on a single device (e.g., the server110), it should be understood that in other implementations, any combination of the elements may be located in separate computing devices. Moreover,FIG.2is intended more as functional description of the various features which are present in a particular implementation as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately inFIG.2could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one implementation to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular implementation.

FIG.3is a block diagram of an example of the device120in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations the device120includes one or more processing units302(e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices and sensors306, one or more communication interfaces308(e.g., USB, FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, SPI, I2C, and/or the like type interface), one or more programming (e.g., I/O) interfaces310, one or more AR/VR displays312, one or more interior and/or exterior facing image sensor systems314, a memory320, and one or more communication buses304for interconnecting these and various other components.

In some implementations, the one or more communication buses304include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors306include at least one of an inertial measurement unit (IMU), an accelerometer, a magnetometer, a gyroscope, a thermometer, one or more physiological sensors (e.g., blood pressure monitor, heart rate monitor, blood oxygen sensor, blood glucose sensor, etc.), one or more microphones, one or more speakers, a haptics engine, one or more depth sensors (e.g., a structured light, a time-of-flight, or the like), and/or the like.

In some implementations, the one or more displays312are configured to present the experience to the user. In some implementations, the one or more displays312correspond to holographic, digital light processing (DLP), liquid-crystal display (LCD), liquid-crystal on silicon (LCoS), organic light-emitting field-effect transitory (OLET), organic light-emitting diode (OLED), surface-conduction electron-emitter display (SED), field-emission display (FED), quantum-dot light-emitting diode (QD-LED), micro-electro-mechanical system (MEMS), and/or the like display types. In some implementations, the one or more displays312correspond to diffractive, reflective, polarized, holographic, etc. waveguide displays. For example, the device120includes a single display. In another example, the device120includes an display for each eye of the user.

In some implementations, the one or more image sensor systems314are configured to obtain image data that corresponds to at least a portion of the physical environment105. For example, the one or more image sensor systems314include one or more RGB cameras (e.g., with a complimentary metal-oxide-semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor), monochrome cameras, IR cameras, event-based cameras, and/or the like. In various implementations, the one or more image sensor systems314further include illumination sources that emit light, such as a flash. In various implementations, the one or more image sensor systems314further include an on-camera image signal processor (ISP) configured to execute a plurality of processing operations on the image data including at least a portion of the processes and techniques described herein.

The memory320includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory320includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory320optionally includes one or more storage devices remotely located from the one or more processing units302. The memory320comprises a non-transitory computer readable storage medium. In some implementations, the memory320or the non-transitory computer readable storage medium of the memory320stores the following programs, modules and data structures, or a subset thereof including an optional operating system330and one or more applications340.

The operating system330includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the applications340are configured to manage and coordinate one or more experiences for one or more users (e.g., a single experience for one or more users, or multiple experiences for respective groups of one or more users).

The applications340include an integration unit342, a segmentation unit344, a mask data unit346, and a 3D model unit348. The integration unit342, the segmentation unit344, the mask data unit346, and the 3D model unit348can be combined into a single application or unit or separated into one or more additional applications or units.

The integration unit342is configured with instructions executable by a processor to obtain image data (e.g., light intensity data, depth data, camera position information, etc.) and integrate (e.g., fuse) the image data using one or more of the techniques disclosed herein. For example, the integration unit342fuses RGB images from a light intensity camera with a sparse depth map from a depth camera (e.g., time-of-flight sensor) and other sources of physical environment information (e.g., camera positioning information from a camera's SLAM system, VIO, or the like) to output a dense depth point cloud of information.

The segmentation unit344is configured with instructions executable by a processor to generate segmentation data of the physical environment using one or more of the techniques disclosed herein. For example, the segmentation unit344obtains a sequence of light intensity images (e.g., RGB) from a light intensity camera (e.g., a live camera feed) and performs a semantic segmentation algorithm to assign semantic labels to recognized features (e.g., walls, doors, floor, windows, etc.) and/or objects (e.g., furniture, appliances, people, etc.) in the image data. The segmentation unit344can then generate segmented data, i.e., semantic image data (e.g., RGB-S), using one or more of the techniques disclosed herein. In some implementations, the segmentation includes confidence levels for each identified feature and/or object for each pixel location.

The mask data unit346is configured with instructions executable by a processor to generate a mask (e.g., a segmentation mask) identifying portions of an image associated with an object (e.g., an object associated with movement) based on the image data using one or more techniques disclosed herein. For example, the mask data unit346obtains depth data from the integration unit610, the segmentation data from the segmentation unit344, and generates a segmentation mask for all identified movable objects (e.g., identifies all locations of identified people for the 3D data to provide to the 3D model unit348). Alternatively, the mask data unit346generates a segmentation mask based on the segmentation data from the segmentation unit344. Alternatively, the mask data unit346generates a segmentation mask based directly on the obtained depth data (e.g., a depth camera on the device120).

In some implementations, the mask data unit346includes a plurality of machine learning units for each specific type of object associated with movement. For example, a class-1 neural network for people, a class-2 neural network for cats, a class-3 neural network for dogs, etc. The plurality of machine learning units can be trained for a different subset of objects such that the mask data unit346can provide accurate masks for each subset of object associated with movement. Alternatively, the mask data can be used to mask (e.g., filter, remove, exclude, etc.) any type of object or feature that is desired to not be included in the 3D model such that the mask data unit346includes a plurality of machine learning units for each specific type of object. Additionally, or alternatively, the mask data unit346can include a machine learning unit trained to identify an object that is moving and creating distortion or noise in the acquired image data and determine to generate a mask for that particular object (e.g., a mechanical device in the room that is generating large movements such as a large ceiling fan).

The 3D model unit348is configured with instructions executable by a processor to obtain 3D data, segmentation data, and mask data, and generate a 3D model using one or more techniques disclosed herein. For example, the 3D model unit348obtains 3D data (e.g., 3D point cloud data) from the integration unit342, obtains segmentation data (e.g., RGB-S data) from the segmentation unit344, obtains mask data from the mask data unit346, other sources of physical environment information (e.g., camera positioning information), and generates a 3D model (e.g., a 3D mesh representation, a 3D point cloud with associated semantic labels, or the like) that excludes objects or features identified by the mask data.

Although these elements are shown as residing on a single device (e.g., the device120), it should be understood that in other implementations, any combination of the elements may be located in separate computing devices. Moreover,FIG.3is intended more as functional description of the various features which are present in a particular implementation as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules (e.g., applications340) shown separately inFIG.3could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one implementation to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular implementation.

FIG.4is a flowchart representation of an exemplary method400that provides measurement data for objects within a physical environment in accordance with some implementations. In some implementations, the method400is performed by a device (e.g., server110or device120ofFIGS.1-3), such as a mobile device, desktop, laptop, or server device. The method400can be performed on a device (e.g., device120ofFIGS.1and3) that has a screen for displaying images and/or a screen for viewing stereoscopic images such as a head-mounted display (HMD). In some implementations, the method400is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method400is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). The 3D model creation process of method400is illustrated with reference toFIGS.5-6.

At block402, the method400obtains depth data including depth values for pixels of a first image. For example, a user captures video while walking around the room to capture images of different parts of the room from multiple perspectives. The depth data can include pixel depth values from a viewpoint and sensor position and orientation data. In some implementations, the depth data is obtained using one or more depth cameras. For example, the one or more depth cameras can acquire depth based on structured light (SL), passive stereo (PS), active stereo (AS), time-of-flight (ToF), and the like. Various techniques may be applied to acquire depth image data to assign each portion (e.g., at a pixel level) of the image. For example, voxel data (e.g., a raster graphic on a 3D grid, with the values of length, width, and depth) may also contain multiple scalar values such as opacity, color, and density. In some implementations, depth data is obtained from sensors or 3D models of the content of an image. Some or all of the content of an image can be based on a real environment, for example, depicting the physical environment105around the device120. Image sensors may capture images of the physical environment105for inclusion in the image and depth information about the physical environment105. In some implementations, a depth sensor on the device120determines depth values for voxels that are determined based on images captured by an image sensor on the device120.

At block404, the method400obtains a segmentation mask associated with a second image, where the segmentation mask identifies portions of the second image associated with an object. The second image includes pixels each having a value provided by a camera (e.g., RGB cameras with a complimentary metal-oxide-semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor, monochrome cameras, IR cameras, event-based cameras, or the like). For example, the device (e.g., device120) may include a light intensity camera (e.g., RGB camera) that captures the second image, a segmentation machine learning model to generate the segmentation mask identifying objects of a particular type (e.g., person, cat) associated with motion (e.g., people frequently move/couches do not). In some implementations, the mask may indicate objects using values 0 or 1. In some implementations, the segmentation machine learning model may be a neural network executed by a neural engine/circuits on the processor chip tuned to accelerate AI software. In some implementations, the segmentation mask may include confidence values at the pixel level. For example, a pixel location may be labeled as 0.8 chair, thus, the segmentation machine learning model is 80% confident that the x,y,z coordinates for that pixel location is a chair. As additional data is obtained, the confidence level at each pixel location may be adjusted.

At block406, the method400generates a 3D model based on the depth data and the mask. For example, the 3D model may be a 3D mesh representation or a 3D point cloud. In some implementations, the segmentation mask may be used to exclude certain depth values, e.g., those corresponding to the same portions of the aligned first and second images, from use in generating or updating the 3D model. In some implementations, the segmentation mask confidence values may be taken into account. For example, if the confidence values from the mask data are greater than or equal to a confidence value threshold (e.g., 70%) for each identified pixel for a movable object (e.g., a person), then the 3D model would exclude those particular pixel locations in the 3D model (e.g., the model of a person sitting on a chair would only include a 3D model of the chair). Additionally, or alternatively, the 3D model unit may determine its own confidence values for each pixel location based on the received data.

In use, for the process400, a user (e.g., user102inFIG.1) may scan a room with a device (e.g., a smartphone such as device120) and the processes described herein would capture image data, identify all features and objects within the environment (e.g., walls, furniture, people, etc.), determine which objects are classified as moving objects, and provide a 3D representation for the physical environment excluding the identified moving objects as it is being scanned by the user. In some implementations, the 3D representation may be automatically displayed and updated on the user device overlaid during a live camera feed. In some implementations, the 3D representation may be provided after some type of user interaction after scanning the physical environment. For example, the user may be shown options of identified “moving” objects, and the user may select or click on the particular objects that the user does not want included in the 3D representation, and the 3D representation would then be displayed with the selected “moving” objects removed. Thus, as shown and discussed below with reference toFIGS.5-6, the mask data unit identifies the objects associated with motion that are to be removed by model unit (e.g., 3D model unit248ofFIG.2, and/or 3D model unit348ofFIG.3).

FIGS.5A-5Bare block diagrams illustrating example mask data generation based on image information in accordance with some implementations. In some implementations, the system flow of the example environments500A,500B is performed on a device (e.g., server110or device120ofFIGS.1-3), such as a mobile device, desktop, laptop, or server device. The system flow of the example environments500A,500B can be displayed on a device (e.g., device120ofFIGS.1and3) that has a screen for displaying images and/or a screen for viewing stereoscopic images such as a head-mounted display (HMD). In some implementations, the system flow of the example environments500A,500B is performed on processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the system flow of the example environments500A,500B is performed on a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory).

In particular,FIG.5Ais a block diagram of an example environment500A in which a mask data unit510(e.g., mask data unit246ofFIG.2, and/or mask data unit346ofFIG.3) can generate masked data512using semantic segmentation data based light intensity image information detected in the physical environment.FIG.5Bis a block diagram of an example environment500B in which a mask data unit510(e.g., mask data unit246ofFIG.2, and/or mask data unit346ofFIG.3) can generate masked data514using obtained 3D data522(e.g., a 3D point cloud) and/or obtained semantic segmentation data530. The masked data512,514, represent the objects that are associated with motion that are to be excluded. The mask data unit510can send the obtained image information excluding the masked data (e.g., movable objects) to a 3D model unit (e.g., 3D model unit246ofFIG.2, and/or 3D model unit346ofFIG.3). Alternatively, the mask data unit510can send the identified pixel locations of the identified objects associated with motion to a 3D model unit, and the 3D model unit can then determine whether or not to include or exclude the identified objects (e.g., based on confidence values).

FIG.6is a system flow diagram of an example environment600in which a system can generate a 3D representation using 3D data and segmentation data based on depth and light intensity image information detected in the physical environment. In some implementations, the system flow of the example environment600is performed on a device (e.g., server110or device120ofFIGS.1-3), such as a mobile device, desktop, laptop, or server device. The system flow of the example environment600can be displayed on a device (e.g., device120ofFIGS.1and3) that has a screen for displaying images and/or a screen for viewing stereoscopic images such as a head-mounted display (HMD). In some implementations, the system flow of the example environment600is performed on processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the system flow of the example environment600is performed on a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory).

The system flow of the example environment600acquires light intensity image data607(e.g., live camera feed from light intensity camera606), depth image data607(e.g., depth image data from depth camera604), and other sources of physical environment information (e.g., camera positioning information from position sensors602) of a physical environment (e.g., the physical environment105ofFIG.1), and generates 3D model data642. The 3D model data could be a 3D representation644representing the surfaces in a 3D environment using a 3D point cloud with associated semantic labels. In some implementations, the 3D model data642is a 3D reconstruction mesh646using a meshing algorithm based on depth information detected in the physical environment that is integrated (e.g., fused) to recreate the physical environment. A meshing algorithm (e.g., a dual marching cubes meshing algorithm, a poisson meshing algorithm, a tetrahedral meshing algorithm, or the like) can be used to generate a mesh representing a room (e.g., physical environment105) and/or object(s) within a room (e.g., wall130, door150, chair140, table142, etc.). In some implementations, for 3D reconstructions using a mesh, to efficiently reduce the amount of memory used in the reconstruction process, a voxel hashing approach is used in which 3D space is divided into voxel blocks, referenced by a hash table using their 3D positions as keys. The voxel blocks are only constructed around object surfaces, thus freeing up memory that would otherwise have been used to store empty space. The voxel hashing approach is also faster than competing approaches at that time, such as octree-based methods. In addition, it supports streaming of data between the GPU, where memory is often limited, and the CPU, where memory is more abundant.

In an example implementation, the environment600includes an image composition pipeline that acquires or obtains data (e.g., image data from image source(s)) for the physical environment. Example environment600is an example of acquiring image data (e.g., light intensity data and depth data) for a plurality of image frames. The image source(s) may include a depth camera604that acquires depth data605of the physical environment, and a light intensity camera606(e.g., RGB camera) that acquires light intensity image data607(e.g., a sequence of RGB image frames).

The example environment600includes an integration unit610that is configured with instructions executable by a processor to obtain the image data (e.g., light intensity data607, depth data605, etc.) and positioning information (e.g., camera pose from position sensors602) and integrate (e.g., fuse) the image data using one or more known techniques. For example, the image integration unit610(e.g., integration unit242ofFIG.2, and/or integration unit342ofFIG.3) receives depth image data605(e.g., sparse depth data) and intensity image data607(e.g., RGB) from the image sources (e.g., light intensity camera606and depth camera604), and integrates the image data and generates 3D data612. The 3D data612can include a dense 3D point cloud614(e.g., imperfect depth maps and camera poses for a plurality of image frames around the object) that is sent to the 3D model unit640. In some implementations, the dense 3D point cloud614is also sent to the mask data unit630. The different size grey dots in the 3D point cloud614represent different depth values detected within the depth data. For example, image integration unit610fuses RGB images from a light intensity camera with a sparse depth map from a depth camera (e.g., time-of-flight sensor) and other sources of physical environment information to output a dense depth point cloud of information. The 3D data422can also be voxelized, as represented by the voxelized 3D point cloud616, where the different shading on each voxel represents a different depth value.

For the positioning information, some implementations include a visual inertial odometry (VIO) system to determine equivalent odometry information using sequential camera images (e.g., light intensity data607) to estimate the distance traveled. Alternatively, some implementations of the present disclosure may include a SLAM system (e.g., position sensors602). The SLAM system may include a multidimensional (e.g., 3D) laser scanning and range measuring system that is GPS-independent and that provides real-time simultaneous location and mapping. The SLAM system may generate and manage data for a very accurate point cloud that results from reflections of laser scanning from objects in an environment. Movements of any of the points in the point cloud are accurately tracked over time, so that the SLAM system can maintain precise understanding of its location and orientation as it travels through an environment, using the points in the point cloud as reference points for the location.

The example environment600further includes a segmentation unit620that is configured with instructions executable by a processor to obtain the light intensity image data (e.g., light intensity data607) and identify and segment wall structures (wall, doors, windows, etc.) and objects (e.g., person, table, teapot, chair, vase, etc.) using one or more known techniques. For example, the segmentation unit620(e.g., segmentation unit244ofFIG.2, and/or segmentation unit344ofFIG.3) receives intensity image data607from the image sources (e.g., light intensity camera606), and generates segmentation data622(e.g., semantic segmentation data such as RGB-S data). For example, the semantic segmentation data624illustrates an example semantically labelled image of the physical environment105inFIG.1. In some implementations, segmentation unit620uses a machine learning model, where a semantic segmentation model may be configured to identify semantic labels for pixels or voxels of image data. In some implementations, the machine learning model is a neural network (e.g., an artificial neural network), decision tree, support vector machine, Bayesian network, or the like.

The example environment600further includes a mask data unit630that is configured with instructions executable by a processor to obtain image data and/or segmentation data, and generates mask data (e.g., a segmentation mask) using one or more known techniques. For example, the mask data unit630(e.g., mask data unit246ofFIG.2, mask data unit346ofFIG.3, and/or mask data unit510ofFIG.5B) receives 3D data612from the integration unit610and/or segmentation data622from the segmentation unit620, and generates mask data632. For example, the segmentation m mask data634illustrates an example semantically labelled image of identified objects associated with movement (e.g., “motion object/person”) from the example physical environment105inFIG.1. In some implementations, mask data unit630uses a machine learning model, where a segmentation mask model may be configured to identify semantic labels for pixels or voxels of image data. A segmentation mask machine learning model may generate the segmentation mask identifying objects of a particular type (e.g., person, cat) associated with motion (e.g., people frequently move, while furniture does not). In some implementations, the mask may indicate objects using values 0 or 1 to indicate whether the information should be included in the 3D model or not (e.g., motion objects). In some implementations, the segmentation machine learning model may be a neural network executed by a neural engine/circuits on the processor chip tuned to accelerate AI software. In some implementations, the segmentation mask may include confidence values at the pixel level. For example, a pixel location may be labeled as 0.8 chair, thus, the system is 80% confident that the x,y,z coordinates for that pixel location is a chair. As additional data is obtained, the confidence level may be adjusted. In some implementations, the machine learning model is a neural network (e.g., an artificial neural network), decision tree, support vector machine, Bayesian network, or the like.

The example environment600further includes a 3D model unit640that is configured with instructions executable by a processor to obtain the 3D data612(e.g., 3D point cloud data614) from the integration unit610, obtain the segmentation data622(e.g., RGB-S data) from the segmentation unit620, obtain the mask data632from the mask data unit630, and obtain camera positioning information (e.g., camera pose data from position sensors602), and generate 3D model data642using one or more techniques. For example, the 3D model unit640(e.g., 3D model unit248ofFIG.2and/or 3D model unit348ofFIG.3) generates a semantically labeled 3D point cloud644, excluding identified motion objects in the mask data, by acquiring the 3D point cloud data614, the mask data632, and the semantic segmentation data622, and using a semantic 3D algorithm that fuses the 3D data and semantic labels. As illustrated inFIG.6, the 3D point cloud644excludes or masks the identified motion objects (e.g., the two people, movable objects104and106, of the physical environment105inFIG.1), as shown as removed by the “motion object masked” label. In some implementations, each semantic label includes a confidence value. For example, a particular point or pixel location may be labeled as an object (e.g., table), and the data point would include x,y,z coordinates and a confidence value as a decimal value (e.g., 0.9 to represent a 90% confidence the semantic label has classified the particular data point correctly). In some implementations, the segmentation mask confidence values may be taken into account. For example, if the confidence values from the mask data are greater than or equal to a confidence value threshold (e.g., 70%) for each identified pixel for a movable object (e.g., a person), then the 3D model would exclude those particular pixel locations in the 3D model (e.g., the model of a person sitting on a chair would only include a 3D model of the chair). Additionally, or alternatively, the 3D model unit may determine its own confidence values for each pixel location based on the received data.

In some implementations, the 3D model data642is a 3D reconstruction mesh646that is generated using a meshing algorithm based on depth information detected in the physical environment that is integrated (e.g., fused) to recreate the physical environment, but excludes the identified motion objects (e.g., the two people, movable objects104and106, of the physical environment105inFIG.1). A meshing algorithm (e.g., a dual marching cubes meshing algorithm, a poisson meshing algorithm, a tetrahedral meshing algorithm, or the like) can be used to generate a mesh representing a room (e.g., physical environment105) and/or object(s) within a room (e.g., wall130, door150, chair140, table142, etc.). In some implementations, for 3D reconstructions using a mesh, to efficiently reduce the amount of memory used in the reconstruction process, a voxel hashing approach is used in which 3D space is divided into voxel blocks, referenced by a hash table using their 3D positions as keys. The voxel blocks are only constructed around object surfaces, thus freeing up memory that would otherwise have been used to store empty space. The voxel hashing approach is also faster than competing approaches at that time, such as octree-based methods. In addition, it supports streaming of data between the GPU, where memory is often limited, and the CPU, where memory is more abundant.

Alternatively, the mask data unit630generates the mask data632based on the segmentation data622without obtaining the 3D data612, such that the 3D model unit640then correlates the mask data632with the obtained 3D data612. Additionally, the segmentation mask may be used to exclude certain depth values, e.g., those corresponding to the same portions of the aligned first and second images, from use in generating or updating the 3D model.

In some implementations, the image composition pipeline may include virtual content (e.g., a virtual box placed on the table135inFIG.1) that is generated for an extended reality (XR) environment. In some implementations, the operating systems230,330includes built in XR functionality, for example, including a XR environment application or viewer that is configured to be called from the one or more applications240,340to display a XR environment within a user interface. For example, the systems described herein may include a XR unit that is configured with instructions executable by a processor to provide a XR environment that includes depictions of a physical environment including real physical objects and virtual content. A XR unit can generate virtual depth data (e.g., depth images of virtual content) and virtual intensity data (e.g., light intensity images (e.g., RGB) of the virtual content). For example, one of the applications240for the server110or applications340for the device120could include a XR unit that is configured with instructions executable by a processor to provide a XR environment that includes depictions of a physical environment including real objects or virtual objects. The virtual objects may be positioned based on the detection, tracking, and representing of objects in 3D space relative to one another based on stored 3D models of the real objects and the virtual objects, for example, using one or more of the techniques disclosed herein.

Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods apparatuses, or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter.

Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing the terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform.

The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provides a result conditioned on one or more inputs. Suitable computing devices include multipurpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general purpose computing apparatus to a specialized computing apparatus implementing one or more implementations of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device.

Implementations of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied for example, blocks can be re-ordered, combined, and/or broken into sub-blocks. Certain blocks or processes can be performed in parallel.

The use of “adapted to” or “configured to” herein is meant as open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Additionally, the use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or value beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting.

It will also be understood that, although the terms “first,” “second,” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first node could be termed a second node, and, similarly, a second node could be termed a first node, which changing the meaning of the description, so long as all occurrences of the “first node” are renamed consistently and all occurrences of the “second node” are renamed consistently. The first node and the second node are both nodes, but they are not the same node.

The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

The foregoing description and summary of the invention are to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined only from the detailed description of illustrative implementations but according to the full breadth permitted by patent laws. It is to be understood that the implementations shown and described herein are only illustrative of the principles of the present invention and that various modification may be implemented by those skilled in the art without departing from the scope and spirit of the invention.