Patent Description:
Embodiments of the present disclosure relate generally to machine learning and, more particularly, but not by way of limitation, to image-based depth estimation.

Depth estimation schemes attempt to determine the depths of objects depicted in images (e.g., an image, video). The depth data can be useful for different image-based tasks, such as augmented reality, image focusing, and face parsing. Some depth detection techniques use external signals (e.g., infrared beams) to bounce off nearby objects to assist in determining the depths of objects in a given image. While these external-signal-based approaches can yield accurate results, many user devices (e.g., a smartphone, a laptop) are not equipped with the necessary hardware (e.g., infrared (IR) laser, IR camera) to enable signal-based depth detection. Determining depths directly from a single image is difficult because of the inherent ambiguities between an object's appearance in an image and its real-world geometry.

<CIT> concerns the generation of a depth map from a single image. An image is displayed on a computer display, where the displayed image corresponds to image data. User input via is received via one or more tools applied to the displayed image, where the user input specifies one or more depth constraints for at least a portion of the image. A depth map for the image data is automatically determined subject to the one or more depth constraints, and a representation of the depth map is displayed on the computer display.

In an article entitled "<NPL> et al discuss the recovery of depth from a single image taken in unconstrained settings ("in the wild"). The authors introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points. They also propose an algorithm that learns to estimate metric depth using annotations of relative depth.

ADMM-Nets are defined over data flow graphs, which are derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model.

The invention is a method, system and machine-readable storage device as defined in the appended claims.

To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the FIG. number in which that element or act is first introduced.

As discussed, determining the depths of objects depicted in an image is difficult. While some external-signal-based approaches can assist in image-based depth detection, many end-user systems (e.g., client devices, smartphones, laptops) lack the specialized equipment required to implement these signal-based approaches.

To this end, an active depth detection system can be implemented to determine depths of objects directly from an image using runtime end-user-provided data (e.g., end-user input data provided to the system after training). According to some approaches, the active depth detection system includes one or more neural networks that are configured to generate a depth map and refine the depth map using end-user-provided depth data (e.g., ordinal pairs, such as a pair of clicks or screen taps on an image).

For example, a client device can display an image and a user can select (e.g., screen-tap, click, or specify image coordinates for) a first point in the image, followed by a second point in the image. The active depth detection system generates a vector from the first point to the second point to indicate a depth detection (e.g., to indicate that the first point corresponds to a part of the image that is closer to the viewer than the part of the image that corresponds to the second point, or vice-versa). The vector can be input into the neural network system which has been trained to update a depth map using the ordinal pair (e.g., the pair of clicks) as constraints.

In some example embodiments, the active depth detection system is trained using end-to-end training techniques (e.g., back propagation). After the model is trained, it can be downloaded for use by different users as part of a user application (e.g., a messaging client application <NUM> discussed below). When the user generates an image, the system can first generate an initial depth map using a base network, such as a Fully Convolutional Residual Neural Network (FCRN). The active depth detection system can then refine the initial depth map using ordinal constraints (e.g., pairs of clicks) that indicate depth directions of imaged regions (e.g., regions in the original image, regions in the initial depth map). In some example embodiments, the active depth detection system implements a recurrent neural network that is configured as an Alternating Direction Method of Multipliers (ADMM) module with multiple layers. The recurrent neural network can be executed over multiple iterations to generate variables processed in the different layers. The output of the recurrent neural network is a refined depth map that can be used for further processing, such as image manipulation. Although in the following examples, the refined depth map is used for image processing, other uses are possible. For example, the active depth detection system can be implemented in an augmented reality system to more accurately generate simulations that modify the appearance of a user's surrounding environment. As an additional example, the active depth detection system can be implemented as part of an autonomous vehicle vision system to ascertain relative depths of objects depicted in one or more images.

<FIG> is a block diagram showing an example messaging system <NUM> for exchanging data (e.g., messages and associated content) over a network. The messaging system <NUM> includes multiple client devices <NUM>, each of which hosts a number of applications including a messaging client application <NUM>. Each messaging client application <NUM> is communicatively coupled to other instances of the messaging client application <NUM> and a messaging server system <NUM> via a network <NUM> (e.g., the Internet). In various embodiments, virtual machine learning can be used by the messaging client application <NUM> and/or an image processing system <NUM> to analyze images sent within the messaging system <NUM> and to use this analysis to provide features within the messaging system <NUM>.

Accordingly, each messaging client application <NUM> is able to communicate and exchange data with another messaging client application <NUM> and with the messaging server system <NUM> via the network <NUM>. The data exchanged between messaging client applications <NUM>, and between a messaging client application <NUM> and the messaging server system <NUM>, include functions (e.g., commands to invoke functions) as well as payload data (e.g., text, audio, video, or other multimedia data).

The messaging server system <NUM> provides server-side functionality via the network <NUM> to a particular messaging client application <NUM>. While certain functions of the messaging system <NUM> are described herein as being performed by either a messaging client application <NUM> or the messaging server system <NUM>, it will be appreciated that the location of certain functionality within either the messaging client application <NUM> or the messaging server system <NUM> is a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the messaging server system <NUM>, but to later migrate this technology and functionality to the messaging client application <NUM> where a client device <NUM> has a sufficient processing capacity.

The messaging server system <NUM> supports various services and operations that are provided to the messaging client application <NUM>. Such operations include transmitting data to, receiving data from, and processing data generated by the messaging client application <NUM>. This data may include message content, client device information, geolocation information, media annotation and overlays, message content persistence conditions, image search data, social network information, and live event information, as examples, some of which rely on information generated by analyzing images sent through the messaging system <NUM>. Data exchanges within the messaging system <NUM> are invoked and controlled through functions available via user interfaces (UIs) of the messaging client application <NUM>.

Turning now specifically to the messaging server system <NUM>, an application programming interface (API) server <NUM> is coupled to, and provides a programmatic interface to, an application server <NUM>. The application server <NUM> is communicatively coupled to a database server <NUM>, which facilitates access to a database <NUM> in which is stored data associated with messages processed by the application server <NUM>. In some embodiments, the database <NUM> may also store results of image processing or details of various trained and untrained support vector machines that may be used by the messaging server system <NUM>.

The API server <NUM> receives and transmits message data (e.g., commands and message payloads) between the client device <NUM> and the application server <NUM>. Specifically, the API server <NUM> provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the messaging client application <NUM> in order to invoke functionality of the application server <NUM>. The API server <NUM> exposes various functions supported by the application server <NUM>, including account registration; login functionality; the sending of messages, via the application server <NUM>, from a particular messaging client application <NUM> to another messaging client application <NUM>; the sending of media files (e.g., images or video) from a messaging client application <NUM> to a messaging server application <NUM> for possible access by another messaging client application <NUM>; the setting of a collection of media data (e.g., a story); the retrieval of such collections; the retrieval of a list of friends of a user of a client device <NUM>; the retrieval of messages and content; the addition and deletion of friends to and from a social graph; the location of friends within the social graph; and application events (e.g., relating to the messaging client application <NUM>).

The application server <NUM> hosts a number of applications and subsystems, including the messaging server application <NUM>, the image processing system <NUM>, a social network system <NUM>, and an active depth system <NUM>, according to some example embodiments. The messaging server application <NUM> implements a number of message processing technologies and functions, particularly related to the aggregation and other processing of content (e.g., textual and multimedia content) included in messages received from multiple instances of the messaging client application <NUM>. As will be described in further detail, the text and media content from multiple sources may be aggregated into collections of content (e.g., called stories or galleries). These collections are then made available, by the messaging server application <NUM>, to the messaging client application <NUM>. Other processor- and memory-intensive processing of data may also be performed server-side by the messaging server application <NUM>, in view of the hardware requirements for such processing.

The application server <NUM> also includes the image processing system <NUM>, which is dedicated to performing various image processing operations, typically with respect to images or video received within the payload of a message at the messaging server application <NUM>.

The social network system <NUM> supports various social networking functions and services and makes these functions and services available to the messaging server application <NUM>. To this end, the social network system <NUM> maintains and accesses an entity graph (e.g., an entity graph <NUM> in <FIG>) within the database <NUM>. Examples of functions and services supported by the social network system <NUM> include the identification of other users of the messaging system <NUM> with whom a particular user has relationships or whom the particular user is "following," and also the identification of other entities and interests of a particular user.

The application server <NUM> is communicatively coupled to a database server <NUM>, which facilitates access to a database <NUM> in which is stored data associated with messages processed by the messaging server application <NUM>.

<FIG> is a block diagram illustrating further details regarding the messaging system <NUM>, according to example embodiments. Specifically, the messaging system <NUM> is shown to comprise the messaging client application <NUM> and the application server <NUM>, which in turn embody a number of subsystems, namely an ephemeral timer system <NUM>, a collection management system <NUM>, an annotation system <NUM>, and the active depth system <NUM>.

The ephemeral timer system <NUM> is responsible for enforcing the temporary access to content permitted by the messaging client application <NUM> and the messaging server application <NUM>. To this end, the ephemeral timer system <NUM> incorporates a number of timers that, based on duration and display parameters associated with a message or collection of messages (e.g., a story), selectively display and enable access to messages and associated content via the messaging client application <NUM>. Further details regarding the operation of the ephemeral timer system <NUM> are provided below.

The collection management system <NUM> is responsible for managing collections of media (e.g., collections of text, image, video, and audio data). In some examples, a collection of content (e.g., messages, including images, video, text, and audio) may be organized into an "event gallery" or an "event story. " Such a collection may be made available for a specified time period, such as the duration of an event to which the content relates. For example, content relating to a music concert may be made available as a "story" for the duration of that music concert. The collection management system <NUM> may also be responsible for publishing an icon that provides notification of the existence of a particular collection to the user interface of the messaging client application <NUM>.

In certain embodiments, compensation may be paid to a user for inclusion of user-generated content in a collection. In such cases, the curation interface <NUM> operates to automatically make payments to such users for the use of their content.

The annotation system <NUM> provides various functions that enable a user to annotate or otherwise modify or edit media content associated with a message. For example, the annotation system <NUM> provides functions related to the generation and publishing of media overlays for messages processed by the messaging system <NUM>. The annotation system <NUM> operatively supplies a media overlay (e.g., a geofilter or filter) to the messaging client application <NUM> based on a geolocation of the client device <NUM>. In another example, the annotation system <NUM> operatively supplies a media overlay to the messaging client application <NUM> based on other information, such as social network information of the user of the client device <NUM>. A media overlay may include audio and visual content and visual effects. Examples of audio and visual content include pictures, texts, logos, animations, and sound effects. An example of a visual effect includes color overlaying. The audio and visual content or the visual effects can be applied to a media content item (e.g., a photo) at the client device <NUM>. For example, the media overlay includes text that can be overlaid on top of a photograph generated by the client device <NUM>. In another example, the media overlay includes an identification of a location (e.g., Venice Beach), a name of a live event, or a name of a merchant (e.g., Beach Coffee House). In another example, the annotation system <NUM> uses the geolocation of the client device <NUM> to identify a media overlay that includes the name of a merchant at the geolocation of the client device <NUM>. The media overlay may include other indicia associated with the merchant. The media overlays may be stored in the database <NUM> and accessed through the database server <NUM>.

In one example embodiment, the annotation system <NUM> provides a user-based publication platform that enables users to select a geolocation on a map and upload content associated with the selected geolocation. The user may also specify circumstances under which particular content should be offered to other users. The annotation system <NUM> generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system <NUM> provides a merchant-based publication platform that enables merchants to select a particular media overlay associated with a geolocation via a bidding process. For example, the annotation system <NUM> associates the media overlay of a highest-bidding merchant with a corresponding geolocation for a predefined amount of time.

<FIG> is a schematic diagram illustrating data <NUM>, which may be stored in the database <NUM> of the messaging server system <NUM>, according to certain example embodiments. While the content of the database <NUM> is shown to comprise a number of tables, it will be appreciated that the data could be stored in other types of data structures (e.g., as an object-oriented database).

The database <NUM> includes message data stored within a message table <NUM>. An entity table <NUM> stores entity data, including an entity graph <NUM>. Entities for which records are maintained within the entity table <NUM> may include individuals, corporate entities, organizations, objects, places, events, and so forth. Regardless of type, any entity regarding which the messaging server system <NUM> stores data may be a recognized entity. Each entity is provided with a unique identifier, as well as an entity type identifier (not shown).

The entity graph <NUM> furthermore stores information regarding relationships and associations between or among entities. Merely for example, such relationships may be social, professional (e.g., work at a common corporation or organization), interest-based, or activity-based.

The database <NUM> also stores annotation data, in the example form of filters, in an annotation table <NUM>. Filters for which data is stored within the annotation table <NUM> are associated with and applied to videos (for which data is stored in a video table <NUM>) and/or images (for which data is stored in an image table <NUM>). Filters, in one example, are overlays that are displayed as overlaid on an image or video during presentation to a recipient user. Filters may be of various types, including user-selected filters from a gallery of filters presented to a sending user by the messaging client application <NUM> when the sending user is composing a message. Other types of filters include geolocation filters (also known as geo-filters) which may be presented to a sending user based on geographic location. For example, geolocation filters specific to a neighborhood or special location may be presented within a user interface by the messaging client application <NUM>, based on geolocation information determined by a Global Positioning System (GPS) unit of the client device <NUM>. Another type of filter is a data filter, which may be selectively presented to a sending user by the messaging client application <NUM>, based on other inputs or information gathered by the client device <NUM> during the message creation process. Examples of data filters include a current temperature at a specific location, a current speed at which a sending user is traveling, a battery life for a client device <NUM>, or the current time.

Other annotation data that may be stored within the annotation table is so-called "lens" data. A "lens" may be a real-time special effect and sound that may be added to an image or a video.

As mentioned above, the video table <NUM> stores video data which, in one embodiment, is associated with messages for which records are maintained within the message table <NUM>. Similarly, the image table <NUM> stores image data associated with messages for which message data is stored in the message table <NUM>. The entity table <NUM> may associate various annotations from the annotation table <NUM> with various images and videos stored in the image table <NUM> and the video table <NUM>.

A story table <NUM> stores data regarding collections of messages and associated image, video, or audio data, which are compiled into a collection (e.g., a story or a gallery). The creation of a particular collection may be initiated by a particular user (e.g., any user for whom a record is maintained in the entity table <NUM>). A user may create a "personal story" in the form of a collection of content that has been created and sent/broadcast by that user. To this end, the user interface of the messaging client application <NUM> may include an icon that is user-selectable to enable a sending user to add specific content to his or her personal story.

A collection may also constitute a "live story," which is a collection of content from multiple users that is created manually, automatically, or using a combination of manual and automatic techniques. For example, a "live story" may constitute a curated stream of user-submitted content from various locations and events. Users whose client devices <NUM> have location services enabled and are at a common location or event at a particular time may, for example, be presented with an option, via a user interface of the messaging client application <NUM>, to contribute content to a particular live story. The live story may be identified to the user by the messaging client application <NUM>, based on his or her location. The end result is a "live story" told from a community perspective.

A further type of content collection is known as a "location story," which enables a user whose client device <NUM> is located within a specific geographic location (e.g., on a college or university campus) to contribute to a particular collection. In some embodiments, a contribution to a location story may require a second degree of authentication to verify that the end user belongs to a specific organization or other entity (e.g., is a student on the university campus).

<FIG> is a schematic diagram illustrating a structure of a message <NUM>, according to some embodiments, generated by a messaging client application <NUM> for communication to a further messaging client application <NUM> or the messaging server application <NUM>. The content of a particular message <NUM> is used to populate the message table <NUM> stored within the database <NUM>, accessible by the messaging server application <NUM>. Similarly, the content of a message <NUM> is stored in memory as "in-transit" or "inflight" data of the client device <NUM> or the application server <NUM>. The message <NUM> is shown to include the following components:.

The contents (e.g., values) of the various components of the message <NUM> may be pointers to locations in tables within which content data values are stored. For example, an image value in the message image payload <NUM> may be a pointer to (or address of) a location within the image table <NUM>. Similarly, values within the message video payload <NUM> may point to data stored within the video table <NUM>, values stored within the message annotations <NUM> may point to data stored in the annotation table <NUM>, values stored within the message story identifier <NUM> may point to data stored in the story table <NUM>, and values stored within the message sender identifier <NUM> and the message receiver identifier <NUM> may point to user records stored within the entity table <NUM>.

<FIG> is a schematic diagram illustrating an access-limiting process <NUM>, in terms of which access to content (e.g., an ephemeral message <NUM>, and associated multimedia payload of data) or a content collection (e.g., an ephemeral message story <NUM>) may be time-limited (e.g., made ephemeral).

An ephemeral message <NUM> is shown to be associated with a message duration parameter <NUM>, the value of which determines an amount of time that the ephemeral message <NUM> will be displayed to a receiving user of the ephemeral message <NUM> by the messaging client application <NUM>. In one embodiment, where the messaging client application <NUM> is an application client, an ephemeral message <NUM> is viewable by a receiving user for up to a maximum of <NUM> seconds, depending on the amount of time that the sending user specifies using the message duration parameter <NUM>.

The message duration parameter <NUM> and the message receiver identifier <NUM> are shown to be inputs to a message timer <NUM>, which is responsible for determining the amount of time that the ephemeral message <NUM> is shown to a particular receiving user identified by the message receiver identifier <NUM>. In particular, the ephemeral message <NUM> will only be shown to the relevant receiving user for a time period determined by the value of the message duration parameter <NUM>. The message timer <NUM> is shown to provide output to a more generalized ephemeral timer system <NUM>, which is responsible for the overall timing of display of content (e.g., an ephemeral message <NUM>) to a receiving user.

The ephemeral message <NUM> is shown in <FIG> to be included within an ephemeral message story <NUM> (e.g., a personal story, or an event story). The ephemeral message story <NUM> has an associated story duration parameter <NUM>, a value of which determines a time duration for which the ephemeral message story <NUM> is presented and accessible to users of the messaging system <NUM>. The story duration parameter <NUM>, for example, may be the duration of a music concert, where the ephemeral message story <NUM> is a collection of content pertaining to that concert. Alternatively, a user (either the owning user or a curator user) may specify the value for the story duration parameter <NUM> when performing the setup and creation of the ephemeral message story <NUM>.

Additionally, each ephemeral message <NUM> within the ephemeral message story <NUM> has an associated story participation parameter <NUM>, a value of which determines the duration of time for which the ephemeral message <NUM> will be accessible within the context of the ephemeral message story <NUM>. Accordingly, a particular ephemeral message <NUM> may "expire" and become inaccessible within the context of the ephemeral message story <NUM>, prior to the ephemeral message story <NUM> itself expiring in terms of the story duration parameter <NUM>. The story duration parameter <NUM>, story participation parameter <NUM>, and message receiver identifier <NUM> each provide input to a story timer <NUM>, which operationally determines whether a particular ephemeral message <NUM> of the ephemeral message story <NUM> will be displayed to a particular receiving user and, if so, for how long. Note that the ephemeral message story <NUM> is also aware of the identity of the particular receiving user as a result of the message receiver identifier <NUM>.

Accordingly, the story timer <NUM> operationally controls the overall lifespan of an associated ephemeral message story <NUM>, as well as an individual ephemeral message <NUM> included in the ephemeral message story <NUM>. In one embodiment, each and every ephemeral message <NUM> within the ephemeral message story <NUM> remains viewable and accessible for a time period specified by the story duration parameter <NUM>. In a further embodiment, a certain ephemeral message <NUM> may expire, within the context of the ephemeral message story <NUM>, based on a story participation parameter <NUM>. Note that a message duration parameter <NUM> may still determine the duration of time for which a particular ephemeral message <NUM> is displayed to a receiving user, even within the context of the ephemeral message story <NUM>. Accordingly, the message duration parameter <NUM> determines the duration of time that a particular ephemeral message <NUM> is displayed to a receiving user, regardless of whether the receiving user is viewing that ephemeral message <NUM> inside or outside the context of an ephemeral message story <NUM>.

The ephemeral timer system <NUM> may furthermore operationally remove a particular ephemeral message <NUM> from the ephemeral message story <NUM> based on a determination that it has exceeded an associated story participation parameter <NUM>. For example, when a sending user has established a story participation parameter <NUM> of <NUM> hours from posting, the ephemeral timer system <NUM> will remove the relevant ephemeral message <NUM> from the ephemeral message story <NUM> after the specified <NUM> hours. The ephemeral timer system <NUM> also operates to remove an ephemeral message story <NUM> either when the story participation parameter <NUM> for each and every ephemeral message <NUM> within the ephemeral message story <NUM> has expired, or when the ephemeral message story <NUM> itself has expired in terms of the story duration parameter <NUM>.

In certain use cases, a creator of a particular ephemeral message story <NUM> may specify an indefinite story duration parameter <NUM>. In this case, the expiration of the story participation parameter <NUM> for the last remaining ephemeral message <NUM> within the ephemeral message story <NUM> will determine when the ephemeral message story <NUM> itself expires. In this case, a new ephemeral message <NUM>, added to the ephemeral message story <NUM>, with a new story participation parameter <NUM>, effectively extends the life of an ephemeral message story <NUM> to equal the value of the story participation parameter <NUM>.

In response to the ephemeral timer system <NUM> determining that an ephemeral message story <NUM> has expired (e.g., is no longer accessible), the ephemeral timer system <NUM> communicates with the messaging system <NUM> (e.g., specifically, the messaging client application <NUM>) to cause an indicium (e.g., an icon) associated with the relevant ephemeral message story <NUM> to no longer be displayed within a user interface of the messaging client application <NUM>. Similarly, when the ephemeral timer system <NUM> determines that the message duration parameter <NUM> for a particular ephemeral message <NUM> has expired, the ephemeral timer system <NUM> causes the messaging client application <NUM> to no longer display an indicium (e.g., an icon or textual identification) associated with the ephemeral message <NUM>.

<FIG> shows example functional engines of an active depth system <NUM>, according to some example embodiments. As illustrated, the active depth system <NUM> comprises an interface engine <NUM>, a training engine <NUM>, a depth engine <NUM>, and a content engine <NUM>. The interface engine <NUM> manages communications with the messaging server application <NUM> to generate user interfaces, receive input data (e.g., click pairs, selection of a button), and generate content (e.g., images, video). The training engine <NUM> is configured to train the model implemented in the depth engine <NUM>. The depth engine <NUM> is configured to generate a depth map from an individual image using the image and one or more ordinal constraints (e.g., click pairs) input by a user. The content engine <NUM> is configured to perform one or more actions using the depth map generated by the depth engine <NUM>. For example, the content engine <NUM> can be configured to apply an image effect to an image using depth information generated by the depth engine <NUM>, and/or overlay one or more items of content on the image.

<FIG> shows a flow diagram of a method <NUM> for implementing an active depth map, according to some example embodiments. At operation <NUM>, the training engine <NUM> trains an active depth system model, such as a network <NUM>, discussed below with reference to <FIG>. Because the network <NUM> is fully differentiable, the network <NUM> can be trained end-to-end using gradient descent. At operation <NUM>, the interface engine <NUM> generates an image. For example, the interface engine <NUM> generates an image using an image sensor of the client device <NUM>. At operation <NUM>, the depth engine <NUM> receives user interaction data. For example, at operation <NUM>, the depth engine <NUM> receives click pair data as a sequence of screen taps on the image depicted on a display device of the client device <NUM>. At operation <NUM>, the depth engine <NUM> generates a refined depth map using the trained network <NUM>, as discussed in further detail below with reference to <FIG> and <FIG>. At operation <NUM>, the content engine <NUM> modifies the image using the refined depth map that is generated by the depth engine <NUM>. For example, at operation <NUM>, the content engine <NUM> removes a background area of the generated image using the refined depth map generated at operation <NUM>. At operation <NUM>, the content engine <NUM> transmit the modified image as an ephemeral message (e.g., an ephemeral message <NUM>) to a network site (e.g., a social media network site) for access by other network site users.

<FIG> shows a flow diagram of an example method <NUM> for utilizing sets of click pair data, according to some example embodiments. In some implementations, the user of the client device <NUM> inputs multiple pairs of clicks, each click pair set indicating a depth direction of a region in an image. The depth engine <NUM> can implement the method <NUM> as a subroutine of operation <NUM>, in which a refined depth map is generated.

At operation <NUM>, the depth engine <NUM> identifies an ordinal pair, such as a pair of clicks input by a user on an image displayed on a client device. At operation <NUM>, the depth engine <NUM> generates a base depth map for refinement. For example, at operation <NUM>, the depth engine <NUM> implements an FCRN to generate an initial depth map from an image (e.g., the image generated at operation <NUM>, <FIG>).

At operation <NUM>, the depth engine <NUM> uses the received ordinal pair to further refine the base depth map. For example, at operation <NUM>, the depth engine <NUM> runs an ADMM module one or more iterations to refine regions of the initial depth map. At operation <NUM>, the depth engine <NUM> determines whether there are additional sets of ordinal pairs. If the user has input additional click pairs, then at operation <NUM> the method <NUM> continues to operation <NUM>, and the depth engine <NUM> further refines the depth map using the additional ordinal pair information in operations <NUM> and <NUM>. For example, a first ordinal pair may increase the depth accuracy of a first region of a depth map (e.g., the lower right corner), and a second ordinal pair may increase the depth accuracy of a second different region in the depth map (e.g., the upper left corner), and so on. Alternatively, returning to operation <NUM>, if the depth engine <NUM> determines that the user has not input further ordinal pairs, the method <NUM> proceeds to operation <NUM>, in which the depth engine <NUM> stores the refined depth map.

<FIG> shows an example network <NUM> for the depth engine <NUM>, according to some example embodiments. As illustrated, an initial image <NUM> is input into a base network <NUM> (e.g., a Fully Convolutional Residual Neural Network (FCRN)) that generates a base depth map <NUM>. <FIG> shows an example image <NUM> and depth map <NUM>. The depth map <NUM> indicates the depth of different areas in the image <NUM> using data values, such as lightness and darkness. For instance, the pixels of the pool table are darker than the pixels of the wall in the depth map <NUM>, which indicates that the pool table is closer to the viewer (e.g., user, camera lens) than the wall depicted behind the pool table. The depth map can be a separate file from its corresponding image, but can also be integrated into the image as extra channel data for each pixel.

Returning to <FIG>, the base depth map <NUM> is input into an ADMM module <NUM>, which operates in several iterations (e.g., iteration <NUM>, iteration <NUM>,. iteration n) to generate a refined depth map <NUM>. Further input into the ADMM module <NUM> is pair data <NUM>, which comprises pairs of points or clicks which specify relative orders between pairs of pixels in a depth direction, as illustrated in <FIG>, discussed below.

In some example embodiments, the ADMM module <NUM> is implemented as a recurrent neural network that implements update rules to generate the refined depth map <NUM>. The pair data <NUM> comprises user input guidance (e.g., clicks to indicate depth directions), which is used as ordinal constraints on the inferred depth estimations. The depth estimation can be modeled as a quadratic programming scheme with linear constraints. In particular, let N be the total number of pixels in an image, and let x and y be the vector representations for the input image and refined depth (respectively) to be solved. The refined depth values y are bounded within a range [<NUM>, D]. Given M pairs of ordinal constraints from user guidance (user click pairs), the objective function for optimizing y is: <MAT> s. <MAT> where <MAT>, I is the identity matrix, and <NUM> and <NUM> are vectors of all <NUM> and <NUM>. Furthermore, fu(y, x) is a unary potential encoding the prediction from a base deep neural network, and fp (yα, x) is a high-order potential encoding the spatial relationship between neighboring pixels. Ay ≤ B encodes the hard constraints for ordinal relations. The first two parts in A and B ensure that the refined depth output is within the valid range [<NUM>, D]. P is an M × N matrix encoding M different ordinal constraints. We use Pkj = <NUM> and Pkj' = -<NUM> if (j, j') is an ordinal pair where k ≤ M.

The unary potentials fu are of the form fu(y, x; w) =<NUM> ∥y - h(x; w)∥<NUM>, which measures the L2 distance between y and h(x; w). For estimating depths, h(x; w) indicates the output from a base depth prediction network (e.g., the base network <NUM>) parameterized by network weights w. Minimizing the unary terms is equivalent to minimizing the mean squared error between refined depths and base network outputs.

The high-order potentials fp are of the form fp(yα, x; w) = hα(x; w)gα(Wα y). Here Wα denotes a transformation matrix for a filtering operation, and hα(x; w) provides per-pixel guidance information that places stronger local smoothness for pixels on low-frequency edges. The hα(x; w) is constant for all the pixels to show improvement from ordinal constraints.

To solve for refined depth values y, the ADMM algorithm is implemented to handle non-differentiable objectives and hard constraints while maintaining fast convergence. Equation <NUM> is reconfigured using auxiliary variables z = {z<NUM>,. In particular: <MAT> s. <MAT> <MAT>.

The augmented Lagrangian of the original objective function is then: <MAT> where ρα is a constant penalty hyperparameter, and λ, ξ are Lagrange multipliers with λ > <NUM>. The variables y, z, λ, ξ are solved by alternating between the following subproblems.

To solve for refined depth y: the y update rule is the derivative of the Lagrangian function with respect to y: <MAT>.

This step uses the term AT λ to encode the ordinal constraints and adjust the outputs from the base network. The depths are refined iteratively in a forward pass through the ADMM network modules.

To solve for auxiliary variables z: let gα(·) = ∥ · ∥<NUM> be the L1 smoothness priors on y and S(a, b) be the soft thresholding function. The z update rules are obtained by solving a Lasso problem: <MAT>.

To solve for Lagrangian multipliers λ and ξ, the update rule for λ is obtained using gradient ascent: <MAT>.

Similarly, for each ξα, we have the gradient ascent update rule: <MAT> where η and τα are the hyperparameters denoting gradient update step sizes.

In some example embodiments, the ADMM module <NUM> is iterative in nature, weights are not shared, and the number of iterations is fixed to allow the ADMM module <NUM> to use convolutional neural networks with customized activation functions.

A call-out <NUM> shows different layers of the ADMM module <NUM>, according to some example embodiments. The ADMM module <NUM> is configured to run an iteration of the above update rules, according to some example embodiments. The filters to encode the transformation Wα are learned via back propagation training. The data tensors zα, ξα, and λ are initialized as zeros.

In some example embodiments, the depth engine <NUM> uses five ADMM modules, which corresponds to running the ADMM module <NUM> for five iterations. Each ADMM instance contains <NUM> transformations Wα (e.g., each convolution layer includes <NUM> filters, each deconvolution layer includes <NUM> layers, etc.). In some example embodiments, since all operations in the ADMM module <NUM> are differentiable, the entire network <NUM> (e.g., the base network <NUM> and ADMM module <NUM>) can be learned end-to-end using gradient descent. In some example embodiments, the network <NUM> implements a standard mean squared error (MSE) as the loss function.

A first layer 935A in the ADMM module <NUM> is configured to solve for refined depth y. Calculating the numerator corresponds to applying a deconvolution <NUM> (e.g., transposed convolution) step on each ρaza - ξa and taking the sum of results together. Calculating the denominator is performed by converting the deconvolution kernels to optical transfer functions and taking the sum. Calculating the final output is performed by first applying a fast Fourier transform (FFT) on the numerator followed by an inverse FFT on the division result.

A second layer 935B in the ADMM module <NUM> solves for auxiliary variables z. This can be done with a convolution layer <NUM> on y using the same filters shared with the deconvolution layer. The convolution layer output is passed (as indicated by pass operator <NUM>) through a non-linear activation layer <NUM> that implements a standard soft thresholding function S(a, b). In practice, we implement this soft thresholding function using two rectified linear unit (ReLU) functions: S(a, b) = ReLU(a - b) - ReLU(-a - b). In some example embodiments, the convolution layer <NUM> does not share weights with the deconvolution layer in order to increase network capacity.

A third layer 935C and fourth layer 935D in the ADMM module <NUM> correspond to gradient ascent steps that solve for Lagrange multipliers λ and ξ, respectively. These steps are implemented as tensor subtraction and summation operations. The updated result of λ after gradient ascent is passed through an activation layer <NUM> (e.g., an additional ReLU layer) to satisfy the non-negative constraint on λ.

<FIG> show example user interfaces for implementing the active depth system <NUM>, according to some example embodiments. As illustrated in <FIG>, a user <NUM> is holding a client device <NUM> which displays a user interface <NUM>. The user <NUM> has generated an image <NUM> using a capture image button <NUM>. To input click pair data, the user <NUM> selects an add points button <NUM>. Turning to <FIG>, the user <NUM> has selected the add points button <NUM> and screen tapped twice to create one click pair set. In particular, the user <NUM> has screen tapped at a point <NUM> followed by screen tapping at a point <NUM> to indicate that the pixel corresponding to the point <NUM> is closer than the pixel corresponding to the point <NUM>. Although in the examples discussed the first point is closer than the second point, it is to be appreciated that the ordinal pair data can be configured in the reverse direction (e.g., the first point indicates that that point is farther away than a subsequent second point). The active depth system <NUM> receives the screen taps and generates an arrow connecting the two points <NUM> and <NUM> to indicate that the direction of depth for that area of the image (e.g., the depicted ceiling above the cashier) is in the direction of the arrow created by the two points. While the ordinal pair in the examples discussed here is generated by a pair of clicks provided by the user, it is to be appreciated that ordinal pair data can be generated in other ways, such as by swipe gestures or by inputting coordinates of a first point and a second point into text input fields.

In response to receiving a click pair (e.g., the points <NUM> and <NUM>), the depth engine <NUM> generates a base depth map of the image <NUM> and further uses the click pair to generate and store a refined depth map using the click pair, as discussed above. Turning to <FIG>, the content engine <NUM> can be configured to generate a modified image <NUM> from the image <NUM> using the generated refined depth map. In particular, the content engine <NUM> uses the refined depth map to identify background areas of the image <NUM> and remove the background areas to generate the modified image <NUM>. Turning to <FIG>, the content engine <NUM> can be further configured to overlay additional content, such as location content <NUM>, on the modified image <NUM>. The user <NUM> can then select a post button <NUM> to publish the modified image <NUM> with the overlay content (e.g., the location content <NUM>) as an ephemeral message on a network site.

<FIG> is a block diagram illustrating an example software architecture <NUM>, which may be used in conjunction with various hardware architectures herein described. <FIG> is a non-limiting example of a software architecture, and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture <NUM> may execute on hardware such as a machine <NUM> of <FIG> that includes, among other things, processors <NUM>, memory <NUM>, and I/O components <NUM>. A representative hardware layer <NUM> is illustrated and can represent, for example, the machine <NUM> of <FIG>. The representative hardware layer <NUM> includes a processing unit <NUM> having associated executable instructions <NUM>. The executable instructions <NUM> represent the executable instructions of the software architecture <NUM>, including implementation of the methods, components, and so forth described herein. The hardware layer <NUM> also includes memory and/or storage modules <NUM>, which also have the executable instructions <NUM>. The hardware layer <NUM> may also comprise other hardware <NUM>.

In the example architecture of <FIG>, the software architecture <NUM> may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software architecture <NUM> may include layers such as an operating system <NUM>, libraries <NUM>, frameworks/middleware <NUM>, applications <NUM>, and a presentation layer <NUM>. Operationally, the applications <NUM> and/or other components within the layers may invoke API calls <NUM> through the software stack and receive a response in the form of messages <NUM>. The layers illustrated are representative in nature, and not all software architectures have all layers. For example, some mobile or special-purpose operating systems may not provide a frameworks/middleware <NUM>, while others may provide such a layer. Other software architectures may include additional or different layers.

The operating system <NUM> may manage hardware resources and provide common services. The operating system <NUM> may include, for example, a kernel <NUM>, services <NUM>, and drivers <NUM>. The kernel <NUM> may act as an abstraction layer between the hardware and the other software layers. For example, the kernel <NUM> may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The drivers <NUM> are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers <NUM> include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration.

The libraries <NUM> provide a common infrastructure that is used by the applications <NUM> and/or other components and/or layers. The libraries <NUM> provide functionality that allows other software components to perform tasks in an easier fashion than by interfacing directly with the underlying operating system <NUM> functionality (e.g., kernel <NUM>, services <NUM>, and/or drivers <NUM>). The libraries <NUM> may include system libraries <NUM> (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematical functions, and the like. In addition, the libraries <NUM> may include API libraries <NUM> such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as MPEG4, H. <NUM>, MP3, AAC, AMR, JPG, or PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries <NUM> may also include a wide variety of other libraries <NUM> to provide many other APIs to the applications <NUM> and other software components/modules.

The frameworks/middleware <NUM> provide a higher-level common infrastructure that may be used by the applications <NUM> and/or other software components/modules. For example, the frameworks/middleware <NUM> may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware <NUM> may provide a broad spectrum of other APIs that may be utilized by the applications <NUM> and/or other software components/modules, some of which may be specific to a particular operating system <NUM> or platform.

The applications <NUM> include built-in applications <NUM> and/or third-party applications <NUM>. Examples of representative built-in applications <NUM> may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application. The third-party applications <NUM> may include an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform, and may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. The third-party applications <NUM> may invoke the API calls <NUM> provided by the mobile operating system (such as the operating system <NUM>) to facilitate functionality described herein.

The applications <NUM> may use built-in operating system functions (e.g., kernel <NUM>, services <NUM>, and/or drivers <NUM>), libraries <NUM>, and frameworks/middleware <NUM> to create user interfaces to interact with users of the system. Alternatively, or additionally, in some systems interactions with a user may occur through a presentation layer, such as the presentation layer <NUM>. In these systems, the application/component "logic" can be separated from the aspects of the application/component that interact with a user.

<FIG> is a block diagram illustrating components of a machine <NUM>, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically, <FIG> shows a diagrammatic representation of the machine <NUM> in the example form of a computer system, within which instructions <NUM> (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine <NUM> to perform any one or more of the methodologies discussed herein may be executed. As such, the instructions <NUM> may be used to implement modules or components described herein. The instructions <NUM> transform the general, non-programmed machine <NUM> into a particular machine <NUM> programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine <NUM> operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine <NUM> may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine <NUM> may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smartphone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions <NUM>, sequentially or otherwise, that specify actions to be taken by the machine <NUM>. Further, while only a single machine <NUM> is illustrated, the term "machine" shall also be taken to include a collection of machines that individually or jointly execute the instructions <NUM> to perform any one or more of the methodologies discussed herein.

The machine <NUM> may include processors <NUM> having individual processors <NUM> and <NUM> (e.g., cores), memory/storage <NUM>, and I/O components <NUM>, which may be configured to communicate with each other such as via a bus <NUM>. The memory/storage <NUM> may include a memory <NUM>, such as a main memory, or other memory storage, and a storage unit <NUM>, both accessible to the processors <NUM> such as via the bus <NUM>. The storage unit <NUM> and memory <NUM> store the instructions <NUM> embodying any one or more of the methodologies or functions described herein. The instructions <NUM> may also reside, completely or partially, within the memory <NUM>, within the storage unit <NUM>, within at least one of the processors <NUM> (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine <NUM>. Accordingly, the memory <NUM>, the storage unit <NUM>, and the memory of the processors <NUM> are examples of machine-readable media.

The I/O components <NUM> may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components <NUM> that are included in a particular machine <NUM> will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components <NUM> may include many other components that are not shown in <FIG>. The I/O components <NUM> are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various example embodiments, the I/O components <NUM> may include output components <NUM> and input components <NUM>. The output components <NUM> may include visual components (e.g., a display such as a plasma display panel (PDP), a light-emitting diode (LED) display, a liquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components <NUM> may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instruments), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

In further example embodiments, the I/O components <NUM> may include biometric components <NUM>, motion components <NUM>, environment components <NUM>, or position components <NUM> among a wide array of other components. For example, the biometric components <NUM> may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components <NUM> may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environment components <NUM> may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components <NUM> may include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components <NUM> may include communication components <NUM> operable to couple the machine <NUM> to a network <NUM> or devices <NUM> via a coupling <NUM> and a coupling <NUM>, respectively. For example, the communication components <NUM> may include a network interface component or other suitable device to interface with the network <NUM>. In further examples, the communication components <NUM> may include wired communication components, wireless communication components, cellular communication components, near field communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices <NUM> may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

For example, the communication components <NUM> may include radio frequency identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional barcodes such as Universal Product Code (UPC) barcode, multi-dimensional barcodes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF415, Ultra Code, UCC RSS-2D barcode, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals).

"CARRIER SIGNAL" in this context refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over the network using a transmission medium via a network interface device and using any one of a number of well-known transfer protocols.

"CLIENT DEVICE" in this context refers to any machine that interfaces to a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, PDA, smartphone, tablet, ultrabook, netbook, multi-processor system, microprocessor-based or programmable consumer electronics system, game console, set-top box, or any other communication device that a user may use to access a network.

"COMMUNICATIONS NETWORK" in this context refers to one or more portions of a network that may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network or a portion of a network may include a wireless or cellular network and the coupling may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including <NUM>, fourth generation wireless (<NUM>) networks, Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long-Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.

"EPHEMERAL MESSAGE" in this context refers to a message that is accessible for a time-limited duration. An ephemeral message <NUM> may be a text, an image, a video, and the like. The access time for the ephemeral message <NUM> may be set by the message sender. Alternatively, the access time may be a default setting or a setting specified by the recipient. Regardless of the setting technique, the message is transitory.

"MACHINE-READABLE MEDIUM" in this context refers to a component, a device, or other tangible media able to store instructions and data temporarily or permanently and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., erasable programmable read-only memory (EPROM)), and/or any suitable combination thereof. The term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions. The term "machine-readable medium" shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., code) for execution by a machine, such that the instructions, when executed by one or more processors of the machine, cause the machine to perform any one or more of the methodologies described herein. Accordingly, a "machine-readable medium" refers to a single storage apparatus or device, as well as "cloud-based" storage systems or storage networks that include multiple storage apparatus or devices. The term "machine-readable medium" excludes signals per se.

"COMPONENT" in this context refers to a device, a physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide for the partitioning or modularization of particular processing or control functions. Components may be combined via their interfaces with other components to carry out a machine process. A component may be a packaged functional hardware unit designed for use with other components and a part of a program that usually performs a particular function of related functions. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A "hardware component" is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner.

In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform certain operations as described herein. A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware components become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors.

It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. Accordingly, the phrase "hardware component" (or "hardware-implemented component") should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time.

Hardware components can provide information to, and receive information from, other hardware components. Accordingly, the described hardware components may be regarded as being communicatively coupled. Where multiple hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware components. In embodiments in which multiple hardware components are configured or instantiated at different times, communications between or among such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware components have access. For example, one hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware component may then, at a later time, access the memory device to retrieve and process the stored output.

Hardware components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein. As used herein, "processor-implemented component" refers to a hardware component implemented using one or more processors. Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented components.

For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented components may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented components may be distributed across a number of geographic locations.

"PROCESSOR" in this context refers to any circuit or virtual circuit (a physical circuit emulated by logic executing on an actual processor) that manipulates data values according to control signals (e.g., "commands," "op codes," "machine code," etc.) and which produces corresponding output signals that are applied to operate a machine. A processor may, for example, be a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), or any combination thereof. A processor may further be a multi-core processor having two or more independent processors (sometimes referred to as "cores") that may execute instructions contemporaneously.

Claim 1:
A computer implemented method comprising:
storing, on a user device, a trained neural network comprising a convolutional neural network (<NUM>) that outputs into a recurrent neural network (<NUM>);
identifying, on the user device, an image (<NUM>) depicting an environment;
receiving (<NUM>; <NUM>), by the user device, an ordinal pair (<NUM>) indicating a direction of depth in the environment depicted in the image (<NUM>);
generating (<NUM>), on the user device, an initial depth map (<NUM>) from the image using the convolutional neural network (<NUM>);
generating (<NUM>; <NUM>), on the user device, an updated depth map (<NUM>) by inputting the received ordinal pair (<NUM>) into the recurrent neural network (<NUM>) that is trained to update outputs of the convolutional neural network; and
storing (<NUM>) the updated depth map (<NUM>).