US20200312008A1 - Semantic texture mapping system - Google Patents

Abstract

A semantic texture map system to generate a semantic texture map based on a 3D model that comprises a plurality of vertices that include coordinate that indicate positions of the plurality of vertices, a UV map, and a semantic segmentation image that comprises a set of semantic labels.

Description

TECHNICAL FIELD
• Embodiments of the present disclosure relate generally to texture mapping, and more particularly, to systems for generating semantic texture maps.
BACKGROUND
• A “texture map” is a two-dimensional (2D) image file that can be applied to the surface of a three-dimensional (3D) model to add color, texture, or other surface details. Texture maps are typically developed to directly correspond with the “UV” coordinates of a 3D model and may be based on photographs or other images. The letters “U” and “V” denote the axes of a 2D texture (because “X,” “Y,” and “Z” are already used to denote the axes of 3D objects).
• For example, when a 3D model, such as a polygon mesh, is created, a UV map that includes a set of UV coordinates can be generated based on the positions of the vertices of the mesh. The UV map therefore comprises a projection of a 2D image upon a surface of the 3D model.
• Although traditional computational texture analysis provides adequate means for producing texture maps to be used in various applications, there remains a disparity between “visual” and “semantic” features of a space. This means they are unsuitable for applications intended for high levels of user interaction.
BRIEF DESCRIPTION OF THE SEVERAL, VIEWS OF THE DRAWINGS
• To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.
• FIG. 1 is a block diagram showing an example messaging system for exchanging data (e.g., messages and associated content) over a network in accordance with some embodiments, wherein the messaging system includes a semantic texture map system.
• FIG. 2 is block diagram illustrating further details regarding a messaging system, according to example embodiments.
• FIG. 3 is a block diagram illustrating various modules of a semantic texture map system, according to certain example embodiments.
• FIG. 4 is a flowchart illustrating a method for generating a texture map that includes semantic labels, according to certain example embodiments.
• FIG. 5 is a flowchart illustrating a method for presenting content based on a semantic feature based on a texture map that include semantic labels, according to certain example embodiments.
• FIG. 6 is a flowchart illustrating a method for applying a semantic texture map to a presentation of an environment at a client device, according to certain example embodiments.
• FIG. 7 is a flow diagram illustrating a method for generating a semantic texture map, according to certain example embodiments.
• FIG. 8 is an interface diagram depicting augmented reality (AR) content presented based on semantic labels of a semantic texture map, according to certain example embodiments.
• FIG. 9 is a block diagram illustrating a representative software architecture, which may be used in conjunction with various hardware architectures herein described and used to implement various embodiments.
• FIG. 10 is a block diagram illustrating components of a machine, 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.
DETAILED DESCRIPTION
• As discussed above, texture mapping is a method for defining details, surface textures, or color information of a 3D model. While existing texture mapping systems are adequate for adding visual details to 3D models, they fail to provide a means for assigning properties and labels to various surfaces of the 3D models, thereby making them less than ideal for applications where high levels of user interaction is to be expected. Example embodiments described herein therefore relate to systems and methods for generating a “semantic” texture map of a 3D object, where the semantic texture map defines a set of semantic segmentation labels of a particular 3D object.
• Example embodiments described herein relate to a semantic texture map system to perform operations that include: accessing an image that comprises a set of image features; retrieving a semantic segmentation image based on the set of image features of the image, the semantic segmentation image comprising a set of semantic labels; generating a 3D model, such as a 3D mesh model, based on the set of image features of the image, the 3D model comprising a plurality of vertices that include coordinate that indicate positions of the plurality of vertices; generating a UV map based on the set of image features and the coordinates of the plurality of vertices of the 3D model, the UV map comprising a set of two-dimensional (2D) texture coordinates based on the coordinates of the plurality of vertices of the 3D mesh model; projecting the semantic segmentation image onto the 3D mesh model based on the 2D texture coordinates of the UV map; mapping the set of semantic labels of the semantic segmentation image to the 2D texture coordinates of the UV map; and generating a semantic texture map that comprises a set of texels, wherein the texels each include one or more semantic labels and a set of 2D texture coordinates.
• “Semantic segmentation” describes computer vision techniques to predict class labels for each pixel of an image. A semantic segmentation image therefore comprises an image that includes a set of class labels that identify elements or objects depicted by the pixels of the image. According to certain embodiments, a semantic segmentation image can be generated by the semantic texture map system through a semantic segmentation neural network.
• For example, the semantic texture map system may receive, as inputs from a plurality of distributed client devices, image data that includes metadata identifying a location or object. The image data may be classified and sorted by the semantic texture map system based on the corresponding image metadata. The semantic texture map system may maintain a database that comprises collections of image data sorted based on a location or object depicted by the image data. The semantic segmentation neural network may then be trained to generate semantic segmentation images that comprise semantic class labels that identify semantic features of pixels in an image, based on the collections of images from the database.
• Semantic features may for example include: contextual features that correspond with a physical object, location, or surface; analogical features that reference some other known category or class; visual features that define visual or graphical properties of a surface or object; as well as material parameters that define properties of a surface or object and which may include a “roughness value,” a “metallic value,” a “specular value,” and a “base color value.”
• Accordingly, responsive to receiving or accessing an image that comprises image metadata, and a set of image features, the semantic texture map system generates or retrieves a semantic segmentation image based on the image (the image features of the image or the image metadata of the image), where in the semantic segmentation image comprises a set of semantic feature labels assigned to the to pixels of the image.
• The semantic texture map system generates a 3D model based on the image accessed by the semantic texture map system. For example, the semantic texture map system may identify a location or object depicted in the image based on the image metadata or the image features and retrieves a collection of image depicted the location or object from a database. The semantic texture map system processes the collection of images through a 3D reconstruction pipeline to obtain a dense 3D mesh (i.e., a 3D model) based on the collection of images that correspond with the location or objected depicted in the image accessed, where the dense 3D mesh comprises a plurality of vertices, where each vertex among the plurality of vertices includes corresponding coordinates. Similarly, the semantic texture map system generates a UV map based on the image and the collection of images, wherein the UV map comprises a set of 2D texture coordinates to project a 2D image upon the 3D mesh. For example, the 3D mesh may be “wrapped” with a 2D image based on the 2D texture coordinates of the UV map.
• The semantic texture map system projects the semantic segmentation image that corresponds with the image upon the 3D model based on the coordinates of the vertices and maps the semantic feature labels of semantic segmentation image to the 2D texture coordinates of the UV map. The resulting semantic texture map therefor comprises a set of texels, where each texel comprises one or more semantic feature labels, and 2D texture coordinates based on the UV map.
• According to certain embodiments, the semantic texture map generated by the semantic texture map system may be applied to one or more objects detected within a presentation of an environment displayed at a client device. For example, the semantic texture map may be associated with a set of coordinates that correspond with an object (i.e., a building, a structure, a sculpture). Responsive to detecting a client device at or within a threshold distance of the object (i.e., based on a geo-fence), the semantic texture map system accesses and applies the semantic texture map to a depiction of the object within a presentation of an environment that includes the object at a display of the client device. For example, the texture map may be applied to the object as an overlay, such that the texels of the texture map occupy corresponding positions of a surface of the object, based on the coordinates of the vertices of the 3D model.
• By applying the texture map that includes the semantic feature labels to the object in the presentation of the environment at the client device, AR content may be accessed and presented within the presentation of the environment based on the semantic feature labels. As discussed above, the semantic feature labels of the texels of the texture map may provide classification information for a feature of an object presented in the presentation of the environment. For example, a semantic feature label may include an identification of semantic information of the texels, such as which texels are “doors,” windows,” “walls,” “roofs,” or “chimney,” as well as providing information about material properties of the features represented by the texels, including but not limited to “hardness,” “softness,” “flexibility,” and “gloss.”
• Doing so enabled the AR content to more realistically interact with the user through the presentation of the space at the client device. As an illustrative example, by labeling which features of a space are “chimneys,” AR content may be accessed and presented which interacts with the feature realistically. For example, an AR, “Santa Claus” may be accessed and presented within a presentation of a space, responsive to detecting semantic feature labels such as “chimney,” and “roof.” In some embodiments, other attributes may be taken into account to select the AR content, such as user profile information, temporal information, seasonal information, and location information.
• FIG. 1 is a block diagram showing anexample messaging system100 for exchanging data (e.g., messages and associated content) over a network. Themessaging system100 includesmultiple client devices102, each of which hosts a number of applications including amessaging client application104. Eachmessaging client application104 is communicatively coupled to other instances of themessaging client application104 and amessaging server system108 via a network106 (e.g., the Internet).
• Accordingly, eachmessaging client application104 is able to communicate and exchange data with anothermessaging client application104 and with themessaging server system108 via thenetwork106. The data exchanged betweenmessaging client applications104, and between amessaging client application104 and themessaging server system108, includes functions (e.g., commands to invoke functions) as well as payload data (e.g., text, audio, video or other multimedia data).
• Themessaging server system108 provides server-side functionality via thenetwork106 to a particularmessaging client application104. While certain functions of themessaging system100 are described herein as being performed by either amessaging client application104 or by themessaging server system108, it will be appreciated that the location of certain functionality either within themessaging client application104 or themessaging server system108 is a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within themessaging server system108, but to later migrate this technology and functionality to themessaging client application104 where aclient device102 has a sufficient processing capacity.
• Themessaging server system108 supports various services and operations that are provided to themessaging client application104. Such operations include transmitting data to, receiving data from, and processing data generated by themessaging client application104. In some. embodiments, this data includes, message content, client device information, geolocation information, media annotation and overlays, message content persistence conditions, social network information, and live event information, as examples. In other embodiments, other data is used. Data exchanges within themessaging system100 are invoked and controlled through functions available via GUIs of themessaging client application104.
• Turning now specifically to themessaging server system108, an Application Program Interface (API)server110 is coupled to, and provides a programmatic interface to, anapplication server112. Theapplication server112 is communicatively coupled to adatabase server118, which facilitates access to adatabase120 in which is stored data associated with messages processed by theapplication server112.
• Dealing specifically with the Application Program Interface (API)server110, this server receives and transmits message data (e.g., commands and message payloads) between theclient device102 and theapplication server112. Specifically, the Application Program Interface (API)server110 provides a set of interfaces (e.g., routines and protocols) that can be called or queried by themessaging client application104 in order to invoke functionality of theapplication server112. The Application Program Interface (API)server110 exposes various functions supported by theapplication server112, including account registration, login functionality, the sending of messages, via theapplication server112, from a particularmessaging client application104 to anothermessaging client application104, the sending of media files (e.g., images or video) from amessaging client application104 to themessaging server application114, and for possible access by anothermessaging client application104, the setting of a collection of media data (e.g., story), the retrieval of a list of friends of a user of aclient device102, the retrieval of such collections, the retrieval of messages and content, the adding and deletion of friends to a social graph, the location of friends within a social graph, opening and application event (e.g., relating to the messaging client application104).
• Theapplication server112 hosts a number of applications and subsystems, including amessaging server application114, animage processing system116, asocial network system122, and a semantictexture map system124. Themessaging server application114 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 themessaging client application104. 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, galleries, or collections). These collections are then made available, by themessaging server application114, to themessaging client application104. Other processor and memory intensive processing of data may also be performed server-side by themessaging server application114, in view of the hardware requirements for such processing.
• Theapplication server112 also includes animage processing system116 that is dedicated to performing various image processing operations, typically with respect to images or video received within the payload of a message at themessaging server application114.
• Thesocial network system122 supports various social networking functions services and makes these functions and services available to themessaging server application114. To this end, thesocial network system122 maintains and accesses anentity graph304 within thedatabase120. Examples of functions and services supported by thesocial network system122 include the identification of other users of themessaging system100 with which a particular user has relationships or is “following,” and also the identification of other entities and interests of a particular user.
• The application server112 s communicatively coupled to adatabase server118, which facilitates access to adatabase120 in which is stored data associated with messages processed by themessaging server application114.
• FIG. 2 is block diagram illustrating further details regarding themessaging system100, according to example embodiments. Specifically, themessaging system100 is shown to comprise themessaging client application104 and theapplication server112, which in turn embody a number of some subsystems, namely anephemeral timer system202, acollection management system204 and anannotation system206.
• Theephemeral timer system202 is responsible for enforcing the temporary access to content permitted by themessaging client application104 and themessaging server application114. To this end, theephemeral timer system202 incorporates a number of timers that, based on duration and display parameters associated with a message, collection of messages, or graphical element, selectively display and enable access to messages and associated content via themessaging client application104. Further details regarding the operation of theephemeral timer system202 are provided below.
• Thecollection management system204 is responsible for managing collections of media (e.g., a media collection that includes 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. Thecollection management system204 may also be responsible for publishing an icon that provides notification of the existence of a particular collection to the user interface of themessaging client application104.
• Thecollection management system204 furthermore includes a curation interface208 that allows a collection manager to manage and curate a particular collection of content. For example, the curation. interface208 enables an event organizer to curate a collection of content relating to a specific event (e.g., delete inappropriate content or redundant messages). Additionally, thecollection management system204 employs machine vision (or image recognition technology) and content rules to automatically curate a content collection. In certain embodiments, compensation may be paid to a user for inclusion of user generated content into a collection. In such cases, the curation interface208 operates to automatically make payments to such users for the use of their content.
• Theannotation system206 provides various functions that enable a user to annotate or otherwise modify or edit media content, such as user support content received by the user to be forwarded or redistributed to one or more recipients. For example, theannotation system206 provides functions related to the generation and publishing of media overlays for messages processed by themessaging system100. Theannotation system206 operatively supplies a media overlay to themessaging client application104 based on a geolocation of theclient device102. In another example, theannotation system206 operatively supplies a media overlay to themessaging client application104 based on other information, such as, social network information of the user of theclient device102. A media overlay may include audio and visual content and visual effects, as well as augmented reality overlays. Examples of audio and visual content include pictures, texts, logos, animations, and sound effects, as well as animated facial models, image filters, and augmented reality media content. 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 or video or live stream) at theclient device102. For example, the media overlay including text that can be overlaid on top of a photograph generated taken by theclient device102. In another example, the media overlay includes an identification of a location overlay (e.g., Venice beach), a name of a live event, or a name of a merchant overlay (e.g., Beach Coffee House). In another example, theannotation system206 uses the geolocation of theclient device102 to identify a media overlay that includes the name of a merchant at the geolocation of theclient device102. The media overlay may include other indicia associated with the merchant. The media overlays may be stored in thedatabase120 and accessed through thedatabase server118.
• In one example embodiment, theannotation system206 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 a particular media overlay should be offered to other users. Theannotation system206 generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation.
• In another example embodiment, theannotation system206 provides a merchant-based publication platform that enables merchants to select a particular media overlay associated with a geolocation. For example, theannotation system206 associates the media overlay of a highest bidding merchant with a corresponding geolocation for a predefined amount of time
• FIG. 3 is a block diagram illustrating components of the semantictexture map system124 that configure the semantictexture map system124 to generate semantic texture maps by performing operations that include: accessing an image that comprises a set of image features; retrieving a semantic segmentation image in response to the accessing the image that comprises the set of image features, where the semantic segmentation image comprises a set of semantic labels; generating a 3D model based on at least the set of image features of the image in response to the accessing the image, wherein the 3D model comprises a plurality of vertices that include coordinates; generating a UV map based on the set of image features and the coordinates of the plurality of vertices of the 3D mesh model, the UV map comprising a set of 2D texture coordinates based on the coordinates of the plurality of vertices; projecting the semantic segmentation image onto the 3D model based on the 2D texture coordinates of the UV map; mapping the set of semantic labels of the semantic segmentation image to the 2D texture coordinates; and generating a texture map that comprises a set of texels based on the semantic labels the UV map, and the 3D mesh model, according to certain example embodiments.
• The semantictexture map system124 is shown as including aimage module302, a3D mesh module304, aUV map module306, and asemantic labeling module308, all configured to communicate with each other (e.g., via a bus, shared memory, or a switch). Any one or more of these modules may be implemented using one or more processors310 (e.g., by configuring such one or more processors to perform functions described for that module) and hence may include one or more of theprocessors310.
• Any one or more of the modules described may be implemented using hardware alone (e.g., one or more of theprocessors310 of a machine) or a combination. of hardware and software. For example, any module described of the semantictexture map system124 may physically include an arrangement of one or more of the processors310 (e.g., a subset of or among the one or more processors of the machine) configured to perform the operations described herein for that module. As another example, any module of the semantictexture map system124 may include software, hardware, or both, that configure an arrangement of one or more processors310 (e.g., among the one or more processors of the machine) to perform the operations described herein for that module. Accordingly, different modules of the semantictexture map system124 may include and configure different arrangements ofsuch processors310 or a single arrangement ofsuch processors310 at different points in time. Moreover, any two or more modules of the semantictexture map system124 may be combined into a single module, and the functions described herein for a single module may be subdivided among multiple modules. Furthermore, according to various example embodiments, modules described herein as being implemented within a single machine, database, or device may be distributed across multiple machines, databases, or devices.
• FIG. 4 is a flowchart illustrating amethod400 for generating a texture map that includes semantic labels, according to certain example embodiments. Operations of themethod400 may be performed by the modules described above with respect toFIG. 3. As shown inFIG. 4, themethod400 includes one ormore operations402,404,406,408,410,412, and414.
• Atoperation402, theimage module302 accesses an image that comprises a set of image features. For example, theimage module302 may receive the image from aclient device102, wherein the image includes an image presented at a display of theclient device102 and depicts a real-world environment. For example, theclient device102 may include one or more cameras configured to generate and stream image data to be presented at the display of theclient device102, or at an auxiliary device (e.g., streaming video data from theclient device102 to a separate display).
• In some embodiments, the image accessed by theimage module302 may include image metadata or other identifying features. For example, the image metadata may identify a location based on geo-location coordinates, or the identifying features may include a barcode or other scannable coded image.
• Atoperation404, thesemantic labeling module308 retrieves a semantic segmentation image that comprises a set of semantic labels, based on the image features of the image. For example, thesemantic labeling module308 may access a repository, such as thedatabase120, to retrieve a semantic segmentation image that corresponds with the image, based on the image features or metadata of the image.
• In some embodiments, thesemantic labeling module308 generates the semantic segmentation image for a location or object based on a semantic segmentation neural network. For example, thesemantic labeling module308 may access an image repository that comprises collections of images associated with a location or object and use the collections of images to train the semantic segmentation neural network to generate a semantic segmentation image that includes a set of semantic labels.
• Atoperation406, the3D mesh module304 generates a 3D mesh model based on the set of image features of the image, where the 3D mesh model comprises a plurality of vertices that include coordinates. For example, the3D mesh module304 may identify an object depicted in the image based on the set of image features of the image metadata, and retrieve a collection of images associated with the object or location, where the collection of images comprises a set of images that depict the object or location depicted by the image accessed by theimage module302. The3D mesh module304 process the images through a 3D reconstruction pipeline to obtain a dense 3D mesh representation of the object or location depicted in the image or collection of images, and a 3D pose relative to the object.
• Atoperation408, theUV map module306 generates a UV map based on the set of image features of the image and the coordinates of the plurality of vertices of the 3D mesh model. The UV map may therefore comprise a set of 2D texture coordinates that correspond with the coordinates of the vertices of the 3D mesh model. By applying a 2D image to the UV map, the 3D mesh model may be “wrapped” by the 2D image.
• A 2D image may be generated based on the image accessed by theimage module302 or based on the collection of images from thedatabase120. For example, the 2D image may include a photo of an object, or an image generated based on the collection of images that depict the object from thedatabase120.
• Atoperation410, thesemantic labeling module308 projects the semantic segmentation image upon the 3D mesh model based on the 2D texture coordinates from the UV map, and atoperation412, maps the set of semantic labels of the semantic segmentation image to the 2D texture coordinates.
• Atoperation414, thesemantic labeling module308 generates a semantic texture map based on the mapping of the semantic labels to the 2D texture coordinates of the UV map. The texture map comprises a set of texels, wherein each texel includes 2D texture coordinates, and one or more semantic labels. The semantic texture map may therefore comprise a mapping of the semantic labels to the surfaces of the 3D mesh based on the 2D texture coordinates.
• In some embodiments, thesemantic labeling module308 may associate the semantic texture map with an object category or location within a database, such as thedatabases120. For example, the semantic texture map may be associated with a set of geolocation coordinates that identify a location of interest, or object at a location of interest. As an illustrative example, the semantic texture map may be generated based on a building or structure at a specific location identified by a set of geo-location coordinates, wherein the semantic labels of the semantic texture map assign semantic feature descriptors to one or more elements of the building or structure. For example, a texel of the semantic feature map may indicate that a particular surface of the building or structure is a doorway, or an exit of the building or structure.
• FIG. 5 is a flowchart illustrating amethod500 for presenting content based on a semantic feature based on a texture map that include semantic labels, according to certain example embodiments. Operations of themethod500 may be performed by the modules described above with respect toFIG. 3. As shown inFIG. 5, themethod500 includes one ormore operations502,504, and506, that may be performed as a part of (e.g., a subroutine) themethod400 depicted inFIG. 4. For example, themethod500 may be performed subsequent to the operations of themethod400.
• For example, subsequent to generating the semantic texture map, the semantictexture map system124 may associate the semantic texture map with a location of interest or object. Responsive to detecting aclient device102 at the location of interest or receiving image data that identifies the object or location of interest, the semantictexture map system124 retrieves the semantic texture map and applies it to the image data received from theclient device102. The semantic texture map may be overlaid upon an image presented at a display of theclient device102, where a position of the semantic texture map in the image is based on features of the image itself. For example, the semantic texture map may be applied to a building depicted in an image presented at theclient device102, where the semantic labels of the semantic texture map identify and assign properties to one or more features of the building (e.g., doorway, exit, window, roof, chimney, bouncy floor).
• Atoperation502, thesemantic labeling module308 assigns a material parameter to a feature class that corresponds with a semantic label from among the set of semantic labels included in a semantic texture map. For example, an administrator of the semantictexture map system124 may assign a material parameter to a class identified by one or more semantic feature labels within thedatabase120. Material parameters may include graphical properties, such as reflectivity or glossiness, as well as physical properties, such as softness or flexibility. The material parameters may be defined based on a set of interaction variables, wherein a texel assigned a parameter may be interacted with by a user or by AR content based on the associated material parameter.
• For example, the material parameter may include a reflectivity value, such that a high reflectivity value may correspond with a mirrored, high reflective surface (i.e., a mirror), while a low reflectivity value would correspond with a non-reflective surface (i.e., a brick wall).
• Atoperation504, subsequent to assigning the material parameters to the class, theimage module302 detects a texel of a semantic texture map that includes a semantic feature label that corresponds with the class. For example, a user of theclient device102 may display a presentation of a location that includes an object. Responsive to detecting theclient device102 at the location, or detecting the object within the presentation of the location, the semantictexture map system124 accesses a semantic texture map that corresponds with the location or object, and applies the semantic texture map to one or more relevant surfaces. For example, the semantic texture map may be overlaid upon the object by positioning a 3D mesh model associated with the object at a position based on the position of the object within the presentation of the environment, and then wrapping the 3D mesh model with the texels of the UV map that includes a set of semantic feature labels that include the feature label associated with the class.
• Atoperation506, AR content may be presented in the presentation of the space based on the material parameter of the texel of the semantic texture map. As an illustrative example, the material parameter may include a high reflectivity value, and the. AR content may include a display of an animated graphical avatar at a position within the presentation of the space. Upon detecting the material parameter of the texel, the AR content may be presented accordingly, such that a reflection of the animated graphical avatar may appear in the texel.
• FIG. 6 is a flowchart illustrating amethod600 for applying a semantic texture map to a presentation of an environment at aclient device102, according to certain example embodiments. Operations of themethod600 may be performed by the modules described above with respect toFIG. 3. As shown inFIG. 6, themethod600 includes one ormore operations602,604, and606, that may be performed as a part of (e.g., a subroutine) themethod400 depicted inFIG. 4. For example, themethod600 may be performed subsequent to generated a semantic texture map based on image data.
• Atoperation602, the semantictexture map system124 receives an input assigning the semantic texture map to a location or target object, within adatabase120. For example, the semantic texture map may be associated with a set of geolocation coordinates that identify a location, or in further embodiments may be assigned to a set of image features that correspond with a target object.
• Atoperation604, the semantictexture map system124 detects aclient device102 at the location identified by the geolocation coordinates associated with the semantic texture map in thedatabase120. For example, a geo-fence may be configured based on the geolocation coordinates associated with the semantic texture map, where the geo-fence encompasses the location identified by the geolocation coordinates.
• Atoperation606, responsive to detecting theclient device102 at the location identified by the geolocation coordinates, the semantictexture map system124 applies the semantic texture map associated with the location to a presentation of an image at theclient device102.
• FIG. 7 is a flow diagram700 illustrating a method for generating a semantic texture map, according to certain example embodiments.
• The semantictexture map system124 generates a semantic texture map based on one or more images that comprise image features. Atoperation705 of the flow diagram700, the semantictexture map system124 accesses a repository, such as thedatabase120, wherein the repository comprises a collection of images depicting a target object or location of interest.
• As seen in the flow diagram700, the collection of images may for example include image depicting a target object, such as thehouse725. Responsive to accessing the collection of images, atoperation710 the various modules of the semantictexture map system124 generates a3D mesh model730 of a target object depicted in the images, by applying the collection of images to a 3D reconstruction pipeline. The3D mesh model730 comprises a set of vertices that include coordinates indicating positions of the vertices relative to one another.
• Atoperation715, as discussed inoperation408 of themethod400, the semantictexture map system124 generates aUV map735 based on the coordinates of the plurality of vertices. TheUV map735 comprises a set of 2D texture coordinates based on the coordinates of the plurality of vertices of the 3D mesh model. As an illustrative example, theUV map735 may be described as an “unwrapped” layer of the3D mesh model730, that defines a set of 2D coordinates which can be used to apply graphics to the3D mesh model730.
• Atoperation720, the semantictexture map system124 generates asemantic segmentation image740 based on the collection of images including theimage725, by applying the collection of images to a semantic segmentation neural network. Thesemantic segmentation image740 generated by the semantictexture map system124 provides semantic feature labels to one or more objects depicted in the collection of images. According to certain embodiments, thesemantic segmentation image740 may include semantic feature labels that describe one or more properties of a texel that include texture properties, physical properties, and graphical properties.
• As discussed inoperation410 of themethod400, thesemantic segmentation image740 is projected upon the3D mesh model725 based on the coordinates from theUV map735, and the semantic feature labels of thesemantic segmentation image740 are mapped to the coordinates of theUV map735 in order to generate a semantic texture map.
• FIG. 8 is an interface diagram800 depictingAR content805,810, and815 presented based on semantic labels, such as the semantic labels from thesemantic segmentation image740 ofFIG. 7, according to certain example embodiments.
• As seen in the interface diagram800,AR content805,810, and815 may be presented based on the semantic feature labels of the semantic texture map generated by the semantictexture map system124. For example, as illustrated inFIG. 8, theAR content805,810, and815 may include graphical elements presented at specific positions in texels displayed an image based on the semantic feature labels of the semantic texture map. For example, the texel820 (emphasized by bolded lines), may include a set of semantic feature labels825, where the semantic feature labels identify properties of the texel. As seen in the interface diagram800, the semantic feature labels825 of thetexel820 may indicate that the texel includes a window. The semanticfeature map system124 may therefore generate and present theAR content815 based on one of more of the semantic feature labels825, providing an added layer of realism to the AR content.
Software Architecture
• FIG. 9 is a block diagram illustrating anexample software architecture906, which may be used in conjunction with various hardware architectures herein described.FIG. 9 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. Thesoftware architecture906 may execute on hardware such as themachine1000 ofFIG. 10 that includes, among other things,processors1004,memory1014, and I/O components1018. Arepresentative hardware layer952 is illustrated and can represent, for example, the machine900 ofFIG. 9. Therepresentative hardware layer952 includes aprocessing unit954 having associatedexecutable instructions904.Executable instructions904 represent the executable instructions of thesoftware architecture906, including implementation of the methods, components and so forth described herein. Thehardware layer952 also includes memory and/or storage modules memory/storage956, which also haveexecutable instructions904. Thehardware layer952 may also compriseother hardware958.
• In the example architecture ofFIG. 9, thesoftware architecture906 may be conceptualized as a stack of layers where each layer provides particular functionality. For example, thesoftware architecture906 may include layers such as anoperating system902,libraries920,applications916 and apresentation layer914. Operationally, theapplications916 and/or other components within the layers may invoke application programming interface (API) API calls908 through the software stack and receive a response as in response to the API calls908. 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/middleware918, while others may provide such a layer. Other software architectures may include additional or different layers.
• Theoperating system902 may manage hardware resources and provide common services. Theoperating system902 may include, for example, akernel922,services924 anddrivers926. Thekernel922 may act as an abstraction layer between the hardware and the other software layers. For example, thekernel922 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. Theservices924 may provide other common services for the other software layers. Thedrivers926 are responsible for controlling or interfacing with the underlying hardware. For instance, thedrivers926 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.
• Thelibraries920 provide a common infrastructure that is used by theapplications916 and/or other components and/or layers. Thelibraries920 provide functionality that allows other software components to perform tasks in an easier fashion than to interface directly with theunderlying operating system902 functionality (e.g.,kernel922,services924 and/or drivers926). Thelibraries920 may include system libraries944 (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematical functions, and the like. In addition, thelibraries920 may includeAm libraries946 such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a 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. Thelibraries920 may also include a wide variety ofother libraries948 to provide many other APIs to theapplications916 and other software components/modules.
• The frameworks/middleware918 (also sometimes referred to as middleware) provide a higher-level common infrastructure that may be used by theapplications916 and/or other software components/modules. For example, the frameworks/middleware918 may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware918 may provide a broad spectrum of other APIs that may be utilized by theapplications916 and/or other software components/modules, some. of which may be specific to aparticular operating system902 or platform.
• Theapplications916 include built-inapplications938 and/or third-party applications940. Examples of representative built-inapplications938 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. Third-party applications940 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 applications940 may invoke the API calls908 provided by the mobile operating system (such as operating system902) to facilitate functionality described herein.
• Theapplications916 may use built in operating system functions (e.g.,kernel922,services924 and/or drivers926),libraries920, and frameworks/middle ware918 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 aspresentation layer914. In these systems, the application/component “logic” can be separated from the aspects of the application/component that interact with a user.
• FIG. 10 is a block diagram illustrating components of amachine1000, 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. 10 shows a diagrammatic representation of themachine1000 in the example form of a computer system, within which instructions1010 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing themachine1000 to perform any one or more of the methodologies discussed herein may be executed. As such, theinstructions1010 may be used to implement modules or components described herein. Theinstructions1010 transform the general,non-programmed machine1000 into aparticular machine1000 programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, themachine1000 operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, themachine1000 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. Themachine1000 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 smart phone, 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 theinstructions1010, sequentially or otherwise, that specify actions to be taken bymachine1000. Further, while only asingle machine1000 is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute theinstructions1010 to perform any one or more of the methodologies discussed herein.
• Themachine1000 may includeprocessors1004, memory memory/storage1006, and I/O components1018, which may be configured to communicate with each other such as via a bus1002. The memory/storage1006 may include amemory1014, such as a main memory, or other memory storage, and astorage unit1016, both accessible to theprocessors1004 such as via the bus1002. Thestorage unit1016 andmemory1014 store theinstructions1010 embodying any one or more of the methodologies or functions described herein. Theinstructions1010 may also reside, completely or partially, within thememory1014, within thestorage unit1016, within at least one of the processors1004 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by themachine1000. Accordingly, thememory1014, thestorage unit1016, and the memory ofprocessors1004 are examples of machine-readable media.
• The I/O components1018 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 components1018 that are included in aparticular machine1000 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 components1018 may include many other components that are not shown inFIG. 10. The I/O components1018 are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limitin2. In various example embodiments, the I/O components1018 may includeoutput components1026 andinput components1028. Theoutput components1026 may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LEI)) 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. Theinput components1028 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 instrument), 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 components1018 may includebiometric components1030,motion components1034,environmental environment components1036, orposition components1038 among a wide array of other components. For example, thebiometric components1030 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. Themotion components1034 may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. Theenvironment components1036 may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometer 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 detection sensors to detection 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. Theposition components1038 may include location sensor components (e.g., a Global Position system (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 components1018 may includecommunication components1040 operable to couple themachine1000 to anetwork1032 ordevices1020 viacoupling1022 andcoupling1024 respectively. For example, thecommunication components1040 may include a network interface component or other suitable device to interface with thenetwork1032. In further examples,communication components1040 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. Thedevices1020 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB)).
• Moreover, thecommunication components1040 may detect identifiers or include components operable to detect identifiers. For example, thecommunication components1040 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 bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via thecommunication components1040, such as, location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting a NFC beacon signal that may indicate a particular location, and so forth.
Glossary
• “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 medium 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, portable digital assistants (PDAs), smart phones, tablets, ultra books, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, 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 other 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 3G, fourth generation wireless (4G) 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.
• “EMPHEMERAL MESSAGE” in this context refers to a message that is accessible for a time-limited duration. An ephemeral message may be a text, an image, a video and the like. The access time for the ephemeral message 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, device or other tangible media able to store instructions and data temporarily or permanently and may include, but is not be 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 (EEPROM)) 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, physical entity or logic having boundaries defined by function or subroutine calls, branch points, application program interfaces (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 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. .1n embodiments in which multiple hardware components are configured or instantiated at different times, communications between 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). The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. 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. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). 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 Application Program Interface (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 Application Specific integrated Circuit (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.
• “TIMESTAMP” in this context refers to a sequence of characters or encoded information identifying when a certain event occurred, for example giving date and time of day, sometimes accurate to a small fraction of a second.

Claims (20)

1. A method comprising:

accessing an image that comprises a set of image features;

retrieving a semantic segmentation image in response to the accessing the image that comprises the set of image features, the semantic segmentation image comprising a set of semantic labels;

generating a three-dimensional (3D) mesh model based on the set of image features of the image, in response to the accessing the image, the 3D mesh model comprising a plurality of vertices that include coordinates;

generating a UV map based on the set of image features and the coordinates of the plurality of vertices of the 3D mesh model, the UV map comprising a set of two-dimensional (2D) texture coordinates based on the coordinates of the plurality of vertices of the 3D mesh model;

projecting the semantic segmentation image onto the 3D mesh model based on the 2D texture coordinates of the UV map;

mapping the set of semantic labels of the semantic segmentation image to the 2D texture coordinates of the UV map based on the projecting the semantic segmentation image onto the 3D mesh model;

generating a texture map that comprises a set of texels based on the semantic labels of the semantic segmentation image and the UV map;

assigning the texture map a set of geolocation coordinates:

detecting a client device at a location identified by the geolocation coordinates; and

delivering the texture map to the client device.

2. The method ofclaim 1, wherein the set of texels include at least a first texel that comprises a semantic label from among the set of semantic labels, and the method further comprises:

receiving an input that assigns a material parameter to a class that corresponds with the semantic label; and

applying the material parameter to the texel.

3. The method ofclaim 2, wherein the material parameter includes one or more of:

a roughness value;

a metallic value;

a specular value; and

a base color value.

4. The method ofclaim 1, wherein the detecting the client device at the location identified by the geolocation coordinates includes:

detecting the client device within a geofence that encompasses the location identified by the geolocation coordinates.

5. The method ofclaim 1, wherein the set of texels include at least a first texel that comprises a semantic label from among the set of semantic labels, and the method further comprises:

receiving a request to assign a material parameter to a class that corresponds with the semantic label from the image; and

causing display of the texel based on the material parameter assigned to the class that corresponds with the semantic label from the image.

6. The method ofclaim 1, wherein the image comprises a depiction of a target object, and the retrieving the semantic segmentation image in response to the accessing the image that comprises the set of image features includes:

training a semantic segmentation neural network based on a plurality of images that depict the target object and the set of semantic labels.

7. The method ofclaim 1, wherein the set of texels include at least a first texel that comprises a semantic label from among the set of semantic labels, and the method further comprises:

retrieving media content correlated with a class identified by the semantic label; and

applying the media content to the first texel.

8. A system comprising:

a memory; and

at least one hardware processor coupled to the memory and comprising instructions that causes the system to perform operations comprising:

accessing an image that comprises a set of image features;

retrieving a semantic segmentation image in response to the accessing the image that comprises the set of image features, the semantic segmentation image comprising a set of semantic labels;

generating a three-dimensional (3D) mesh model based on the set of image features of the image, in response to the accessing the image, the 3D mesh model comprising a plurality of vertices that include coordinates;

generating a UV map based on the set of image features and the coordinates of the plurality of vertices of the 3D mesh model, the UV map comprising a set of two-dimensional (2D) texture coordinates based on the coordinates of the plurality of vertices of the 3D mesh model;

projecting the semantic segmentation image onto the 3D mesh model based on the 2D texture coordinates of the UV map;

mapping the set of semantic labels of the semantic segmentation image to the 2D texture coordinates of the UV map based on the projecting the semantic segmentation image onto the 3D mesh model;

generating a texture map that comprises a set of texels based on the semantic labels of the semantic segmentation image and the UV map;

assigning the texture map a set of geolocation coordinates;

detecting a client device at a location identified by the geolocation coordinates; and

delivering the texture map to the client device.

9. The system ofclaim 8, wherein the set of texels include at least a first texel that comprises a semantic label from among the set of semantic labels, and the instructions further comprise:

receiving an input that assigns a material parameter to a class that corresponds with the semantic label; and

applying the material parameter to the texel.

10. The system ofclaim 9, wherein the material parameter includes one or more of:

a roughness value;

a metallic value;

a specular value; and

a base color value.

11. The system ofclaim 8, wherein the detecting the client device at the location identified by the geolocation coordinates includes:

detecting the client device within a geofence that encompasses the location identified by the geolocation coordinates.

12. The system ofclaim 8, wherein the set of texels include at least a first texel that comprises a semantic label from among the set of semantic labels, and the instructions further comprise:

receiving a request to assign a material parameter to a class that corresponds with the semantic label from the image; and

causing display of the texel based on the material parameter assigned to the class that corresponds with the semantic label from the image.

13. The system ofclaim 8, wherein the image comprises a depiction of a target object, and the retrieving the semantic segmentation image in response to the accessing the image that comprises the set of image features includes:

training a semantic segmentation neural network based on a plurality of images that depict the target object and the set of semantic labels.

14. The system ofclaim 8, wherein the set of texels include at least a first texel that comprises a semantic label from among the set of semantic labels, and the instructions further comprise:

retrieving media content correlated with a class identified by the semantic label; and

applying the media content to the first texel.

15. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

accessing an image that comprises a set of image features;

retrieving a semantic segmentation image in response to the accessing the image that comprises the set of image features, the semantic segmentation image comprising a set of semantic labels;

generating a three-dimensional (3D) mesh model based on the set of image features of the image, in response to the accessing the image, the 3D mesh model comprising a plurality of vertices that include coordinates;

generating a UV map based on the set of image features and the coordinates of the plurality of vertices of the 3D mesh model, the UV map comprising a set of two-dimensional (2D) texture coordinates based on the coordinates of the plurality of vertices of the 3D mesh model;

projecting the semantic segmentation image onto the 3D mesh model based on the 2D texture coordinates of the UV map;

mapping the set of semantic labels of the semantic segmentation image to the 2D texture coordinates of the UV map based on the projecting the semantic segmentation image onto the 3D mesh model;

generating a texture map that comprises a set of texels based on the semantic labels of the semantic segmentation image and the UV map;

assigning the texture map a set of geolocation coordinates;

detecting a client device at a location identified by the geolocation coordinates; and

delivering the texture map to the client device.

16. The non-transitory machine-readable storage medium ofclaim 15, wherein the set of texels include at least a first texel that comprises a semantic label from among the set of semantic labels, and the instructions further comprise:

receiving an input that assigns a material parameter a class that corresponds with the semantic label; and

applying the material parameter to the texel.

17. The non-transitory machine-readable storage medium ofclaim 16, wherein the material parameter includes one or more of:

a roughness value;

a metallic value;

a specular value; and

a base color value.

18. The non-transitory machine-readable storage medium ofclaim 15, wherein the detecting the client device at the location identified by the geolocation coordinates includes:

detecting the client device within a geofence that encompasses the location identified by the geolocation coordinates.

19. The non-transitory machine-readable storage medium ofclaim 15, wherein the set of texels include at least a first texel that comprises a semantic label from among the set of semantic labels, and the instructions further comprise:

receiving a request to assign a material parameter to a class that corresponds with the semantic label from the image; and

causing display of the texel based on the material parameter assigned to the class that corresponds with the semantic label from the image.

70. The non-transitory machine-readable storage medium ofclaim 15, wherein the image comprises a depiction of a target object, and the retrieving the semantic segmentation image in response to the accessing the image that comprises the set of image features includes:

training a semantic segmentation neural network based on a plurality of images that depict the target object and the set of semantic labels.

Images