Patent Publication Number: US-2021174585-A1

Title: Three-dimensional modeling toolkit

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This patent application is a continuation in part of U.S. patent application Ser. No. 16/580,868, filed on Sep. 24, 2019, which claims the benefit of U.S. Provisional Patent Application No. 62/849,286, filed May 17, 2019, both of which are incorporated in their entireties by reference. 
    
    
     TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to three-dimensional (3D) modeling, and more particularly, to systems for generating 3D models. 
     BACKGROUND 
     3D modeling is the process of developing a mathematical representation of a surface of an object in three dimensions, via specialized sensors and software. 3D models represent the surfaces of objects using a collection of points in 3D space, connected by various geometric entities such as triangles, lines, and curved surfaces. 
     3D models can be generated by a 3D scanner, which can be based on many different technologies, each with their own limitations, advantages, and costs. 
    
    
     
       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 3D modeling system for exchanging data (e.g., messages and associated content) over a network in accordance with some embodiments, wherein the 3D modeling system includes a 3D modeling toolkit. 
         FIG. 2  is a block diagram illustrating various modules of a 3D modeling toolkit, according to certain example embodiments. 
         FIG. 3  is a flowchart illustrating a method for generating and causing display of a 3D model based on a point cloud, according to certain example embodiments. 
         FIG. 4  is a flowchart illustrating a method for preparing a training data set for a machine learned model, according to certain example embodiments. 
         FIG. 5  is a flowchart illustrating a method for presenting a value based on a point cloud, according to certain example embodiments. 
         FIG. 6  is an interface flow diagram illustrating interfaces presented by a 3D modeling toolkit, according to certain example embodiments. 
         FIG. 7  is a diagram depicting a labeled point cloud, according to certain example embodiments. 
         FIG. 8  is a diagram depicting a 3D model retrieved based on a point cloud, 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. 
         FIG. 11  is a block diagram illustrating components of a 3D modeling toolkit that configure the 3D modeling toolkit to generate a 3D model based on a voxel grid, according to certain example embodiments. 
         FIG. 12  is a flowchart illustrating a method for generating a 3D model of an object based on a voxel grid, according to certain example embodiments. 
         FIG. 13  is a flowchart illustrating a method for generating a 3D model of a dental arch based on a voxel grid, according to certain example embodiments. 
         FIG. 14  is a flowchart illustrating a method for identifying teeth depicted in an image, according to certain example embodiments. 
         FIG. 15  is a flowchart illustrating a method for generating a 3D model of a dental arch from a voxel grid, according to certain example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     As discussed above, 3D modeling is the process of developing a mathematical representation of a surface of an object in three dimensions, via specialized sensors and software. While existing methods of generating 3D models are functionally effective, they are often difficult and inconvenient to apply in a number of use cases. As a result, a more user-friendly approach is needed. 
     Example embodiments described herein relate to a system that includes a 3D scanning toolkit to perform operations that include: accessing a first data stream at a client device, wherein the first data stream comprises at least image data; applying a bit mask to the first data stream, the bit mask identifying a portion of the image data; accessing a second data stream at the client device, the second data stream comprising depth data associated with the portion of the image data; generating a point cloud based on the depth data, the point cloud comprising a set of data points that define surface features of an object depicted in the first data stream; and causing display of a visualization of the point cloud upon a presentation of the first data stream at the client device. 
     According to some example embodiments, the first data stream and second data stream accessed at the client device may comprise RGB-D data, wherein each data point comprises an RGB component as well as a depth component. The depth data from the data stream indicates a distance between an image plane and an objected depicted by the data stream, where the image plane is identified as the plane of a display monitor or device users to view an image rendered based on the data stream. 
     In some example embodiments, responsive to accessing the first data stream at the client device, the system accesses and applies a bit-mask to the first data stream, wherein the bit-mask defines which data points of the data stream to scan for depth data. For example, the bit-mask may specify areas depicted by the data stream “to be scanned,” or “not to be scanned,” (i.e., sets areas depicted by the data stream that are not within the bit-mask to a null value) based on attributes of the data points. As an illustrative example, the data stream may depict a person, and the bit-mask may be configured to mask out everything but the person&#39;s head, or even specific portions of the person&#39;s head (i.e., just circumference of top of head) such that depth data indicating surface features of the person&#39;s head is collected. 
     In some embodiments, the 3D modeling toolkit may provide an interface to enable a user to provide a selection of one or more bit-masks to be applied to a data stream, wherein each of the one or more bit-masks may correspond with a different object or category. The data stream may comprise image data (e.g., pictures or video) that depicts one or more objects or people. The bit-masks may therefore be organized based on object categories, or measurement types. For example, a bit mask may be associated with a measurement category for “helmet,” or “glasses,” wherein the corresponding bit-masks mask out the pixels not needed for the measurements. Accordingly, a bit-mask associated with the “helmet” measurement category may filter out everything in the image but a person&#39;s head (or specific portions of a person&#39;s head). In further embodiments, the 3D modeling toolkit may perform one or more image recognition techniques to identify objects depicted in the image data of a data-stream in order to automatically select one or more bit-masks to present to a user of the 3D modeling toolkit as recommendations. 
     Based on the bit-mask applied to the first data stream, the system accesses a portion of a second data stream that comprises depth data, wherein the portion of the second data stream is based on the bit-mask. In such embodiments, the 3D modeling toolkit may access portions of the second data stream that correspond with the pixels indicated based on the bit-mask applied to the first data stream. For example, the bit-mask may assign a binary value to each pixel of an image generated based on the image data, to indicate if the system “should,” or “should not” access the second data stream to scan a particular area depicted by the image data of the first data stream. 
     Based on the depth data of the second data stream, the system generates a point cloud. As discussed herein, a point cloud is a set of data points in a space which depicts the external surfaces of objects. In some example embodiments, the point cloud may be converted into a 3D model. For example, the point cloud may be converted into a polygon mesh model, a triangle mesh model, a non-uniform rational basis spline (NURBS) surface model, or a CAD model through one or more surface reconstruction techniques. 
     The system causes display of a visualization of the point cloud within a presentation of the image data from the first data stream at the client device. The visualization may for example be based on the 3D model generated based on the point cloud. 
     According to certain embodiments, the system saves the 3D model generated based on the point cloud at a memory location at the client device, or in some embodiments at a remote database. For example, the system may present an option to save the 3D model in the presentation of the image data at the client device. 
     In some example embodiments, the 3D modeling toolkit may provide one or more interfaces to generate training data for a machine learned model. For example, the 3D modeling toolkit may access a memory repository that comprises one or more 3D models generated based on point clouds and provide an interface to display presentations of the 3D models at a client device. A user of the client device may provide semantic labels to be applied to the 3D models through the one or more interfaces. The labeled 3D models may then be utilized to train a machine learned model. 
     For example, a machine learned model may be fit on a training dataset, wherein the training dataset is generated based on the point clouds collected by the 3D modeling toolkit. The machine learned model may then be trained using a supervised learning method. 
       FIG. 1  is a block diagram showing an example modeling system  100  for exchanging data over a network. The modeling system  100  include one or more client devices  102  which host a number of applications including a client application  104 . Each client application  104  is communicatively coupled to other instances of the client application  104  and a server system  108  via a network  106  (e.g., the Internet). 
     Accordingly, each client application  104  is able to communicate and exchange data with another client application  104  and with the server system  108  via the network  106 . The data exchanged between client applications  104 , and between a client application  104  and the server system  108 , includes functions (e.g., commands to invoke functions) as well as payload data (e.g., text, audio, video or other multimedia data). 
     The server system  108  provides server-side functionality via the network  106  to a particular client application  104 . While certain functions of the modeling system  100  are described herein as being performed by either a client application  104  or by the server system  108 , it will be appreciated that the location of certain functionality either within the client application  104  or the server system  108  is a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the server system  108 , but to later migrate this technology and functionality to the client application  104  where a client device  102  has a sufficient processing capacity. 
     The server system  108  supports various services and operations that are provided to the client application  104 . Such operations include transmitting data to, receiving data from, and processing data generated by the client application  104 . In some embodiments, this data includes, image data, Red-blue-green (RBG) data, depth data, inertial measurement unit (IMU) data, client device information, geolocation information, as examples. In other embodiments, other data is used. Data exchanges within the modeling system  100  are invoked and controlled through functions available via GUIs of the client application  104 . 
     Turning now specifically to the server system  108 , an Application Program Interface (API) server  110  is coupled to, and provides a programmatic interface to, an application server  112 . The application server  112  is communicatively coupled to a database server  118 , which facilitates access to a database  120  in which is stored data associated with messages processed by the application server  112 . 
     Dealing specifically with the Application Program Interface (API) server  110 , this server receives and transmits data between the client device  102  and the application server  112 . Specifically, the Application Program Interface (API) server  110  provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the client application  104  in order to invoke functionality of the application server  112 . The Application Program Interface (API) server  110  exposes various functions supported by the application server  112 , including account registration, login functionality, the sending of messages or content, via the application server  112 , from a particular client application  104  to another client application  104 , the sending of media files (e.g., images or video) from a client application  104  to the server application  114 , and for possible access by another client application  104 , opening and application event (e.g., relating to the client application  104 ). 
     The application server  112  hosts a number of applications and subsystems, including a server application  114 , an image processing system  116 , and a 3D modeling toolkit  124 . The server application  114  implements a number of image processing technologies and functions, particularly related to the aggregation and other processing of content (e.g., image data) received from multiple instances of the client application  104 . As will be described in further detail, the image data from multiple sources may be aggregated into collections of content. These collections are then made available, by the server application  114 , to the client application  104 . Other processor and memory intensive processing of data may also be performed server-side by the messaging server application  114 , in view of the hardware requirements for such processing. 
     The application server  112  also includes an image processing system  116  that is dedicated to performing various image processing operations, typically with respect to images or video received from one or more client devices  102  at the messaging server application  114 . 
     The application server  112  is communicatively coupled to a database server  118 , which facilitates access to a database  120  in which is stored data associated with image data processed by the messaging server application  114 . 
       FIG. 2  is a block diagram illustrating components of the 3D modeling toolkit  124  that configure the 3D modeling toolkit  124  to generate a 3D model based on a point cloud, according to certain example embodiments. 
     The 3D modeling toolkit  124  is shown as including an image module  202 , a bit mask module  204 , a depth data module  206 , a 3D model module  208 , and an analysis module  210 , 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 processors  212  (e.g., by configuring such one or more processors to perform functions described for that module) and hence may include one or more of the processors  212 . 
     Any one or more of the modules described may be implemented using hardware alone (e.g., one or more of the processors  212  of a machine) or a combination of hardware and software. For example, any module described of the 3D modeling toolkit  124  may physically include an arrangement of one or more of the processors  212  (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 3D modeling toolkit  124  may include software, hardware, or both, that configure an arrangement of one or more processors  212  (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 3D modeling toolkit  124  may include and configure different arrangements of such processors  212  or a single arrangement of such processors  212  at different points in time. Moreover, any two or more modules of the 3D modeling toolkit  124  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. 3  is a flowchart illustrating a method  300  for generating and causing display of a 3D model at a client device  102 , according to certain example embodiments. Operations of the method  300  may be performed by the modules described above with respect to  FIG. 2 . As shown in  FIG. 3 , the method  300  includes one or more operations  302 ,  304 ,  306 , and  308 . 
     At operation  302 , the image module  202  accesses a first data stream at the client device  102 , wherein the first data stream comprises image data. For example, the image data may include RGB data. 
     At operation  304 , the bit mask module  204  applies a bit mask to the first data stream, wherein the bit mask identifies a portion of the image data. In some embodiments, the bit mask module  204  may access the bit mask based on an input received from the client device  102  or based on attributes of the image data from the first data stream. For example, the bit mask may be selected from among a plurality of bit masks, wherein each bit mask among the plurality of bit masks is configured based on features of image data. 
     In some embodiments, the bit mask module  204  may apply a machine learned model to identify the portion of the image data. For example, as will be discussed in more detail in the method  400  of  FIG. 4 , the bit mask module  204  may access a machine learned model based on attributes of the image data, wherein the machine learned model is trained to apply one or more semantic labels to the image data, wherein the one or more semantic labels may indicate regions within the image data to scan or not. 
     For example, the bit mask may assign a binary pixel value to areas in the image data based on features of the image data. By doing so, some areas in the image data (i.e., those areas assigned a 0 pixel value) may be “masked,” indicating that those areas are not to be scanned, while other areas (i.e., those areas assigned a 1 pixel values) are scanned for depth data. 
     At operation  306 , the depth data module  206  accesses a second data stream at the client device  102 , wherein the second data stream comprises depth data associated with the portion of the image data identified based on the bit mask applied to the image data. 
     At operation  308 , the depth data module  206  generates a point cloud based on the depth data, wherein the point cloud comprises a set of data points that define surface features of an object depicted in the first data stream. At operation  310 , the 3D modeling module  208  generates and causes display of a visualization of the point cloud upon a presentation of the first data stream at the client device  102 . 
       FIG. 4  is a flowchart illustrating a method  400  for preparing a training data set for a machine learned model, according to certain example embodiments. Operations of the method  400  may be performed by the modules described above with respect to  FIG. 2 . As shown in  FIG. 4 , the method  400  includes one or more operations  402 ,  404 ,  406 , and  408 , that may be performed as a part of (e.g., a subroutine, or subsequent to) the method  300  depicted in  FIG. 3 . 
     According to certain example embodiments, subsequent to operation  308  of the method  300 , wherein a point cloud is generated by the depth module  206 , at operation  402  the 3D modeling toolkit  124  receives an input that selects a subset of the set of data points of the point cloud. For example, the 3D modeling toolkit  124  may cause display of an interface to receive inputs selecting the subset of the set of data points, wherein the inputs may for example include an input that “paints” the subset of the set of data points with a cursor, or in some embodiments through a tactile input. 
     At operation  404  the 3D modeling toolkit  124  applies a label to the subset of the set of data point identified based on the input. The label may include a semantic label, or a classification. For example, semantic labeling 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.” 
     At operation  406 , the 3D modeling toolkit  124  generates a training dataset based on the label and the subset of the set of data points identified based on the input. 
     At operation  408 , the 3D modeling toolkit  124  fits a machine learned model to the training dataset. Accordingly, the machine learned model may be trained to apply semantic labels to portions of image data, wherein the semantic labels include bit-mask values (i.e., binary values indicating to scan or not scan). 
       FIG. 5  is a flowchart illustrating a method  500  for presenting a value based on a point cloud, according to certain example embodiments. Operations of the method  500  may be performed by the modules described above with respect to  FIG. 2 . As shown in  FIG. 5 , the method  500  includes one or more operations  502 ,  504 ,  506 ,  508 ,  510 ,  512 , and  514 , that may be performed as a part of (e.g., a subroutine, or subsequent to) the method  300  depicted in  FIG. 3 . 
     At operation  502 , the analysis module  210  accesses the point cloud generated by the depth data module  206  at operation  308  of the method  300 . At operation  504 , the analysis module  210  identifies a plurality of landmarks based on the point cloud. 
     Responsive to identifying the plurality of landmarks, at operation  506  the analysis module  210  determines a classification associated with the landmarks, and at operation  508 , causes the 3D model module  208  to retrieve a 3D model associated with the classification and the plurality of landmarks. For example, the database  120  may comprise a collection of 3D models accessible by the 3D modeling toolkit  124 , wherein each 3D model among the collection of 3D models is associated with one or more landmarks. 
     At operation  510 , the 3D model module  208  applies the 3D model to a position in a 3D space relative to the point cloud based on at least the plurality of landmarks of the point cloud. 
     At operation  512 , the analysis module  210  generates a value based on the position of the 3D model relative to the point cloud, and at operation  514  causes display of the value at the client device  102 . 
     As an illustrative example from a user perspective, the point cloud may depict a 3D representation of a human head. The analysis module  210  may analyze the point cloud to detect key landmarks (e.g., facial landmarks, etc.) for alignment and classification, and to fill missing portions of the point cloud by using machine learning. 
     Responsive to analyzing the point cloud and identifying the landmarks, the 3D model module  208  retrieves a 3D model of a helmet, and positions the 3D model of the helmet at a position relative to the point cloud that depicts the human head in order to calculate distances between the landmarks of the point cloud and the 3D model according to a geometric algorithm. The 3D model of the helmet and the point cloud are analyzed further in order to estimate sizing (i.e., a value), which can then be presented at a client device. 
     In some embodiments, the analysis module  210  may filter a collection based on the value. For example, the collection may comprise a plurality of objects with associated size values. The analysis module  210  may access the collection and filter the collection based on the value generated based on the position of the 3D model relative to the point cloud. In some embodiments, the filtered collection may then be presented at the client device  102 . 
       FIG. 6  is an interface flow diagram  600  illustrating interfaces presented by the 3D modeling toolkit  124 , according to certain example embodiments, and as discussed in the method  300  depicted in  FIG. 3 . 
     Interface  602  depicts an interface to initiate a 3D scan. For example, a user of the 3D scanning toolkit  124  may provide an input through the interface element  608  that causes one or more modules of the 3D scanning toolkit  124  to initiate a 3D scan. 
     Interface  604  depicts a presentation of depth data  610  based on a second data stream, as discussed in operation  306  of the method  300 . The depth data provides an indication of a distance of any given point to a reference position (i.e., a camera of the client device  102 ). 
     Interface  606  depicts a 3D model  612  generated based on a first data stream (i.e., image data), and a second data stream (i.e., depth data) presented at a client device  102 . According to certain embodiments, the 3D modeling toolkit  124  may save the 3D model  612  at a memory location at the client device  102 , or in some embodiments at a remote database such as the database  120 . 
       FIG. 7  is a diagram  700  depicting a labeled point cloud  702 , according to certain example embodiments, and as discussed in the method  400  depicted in  FIG. 4 . As seen in the diagram  700 , a user of the 3D modeling toolkit  124  may provide input applying one or more labels to the point cloud  702 . 
     As seen in the diagram  700 , and as discussed in operation  308  of the method  300  depicted in  FIG. 3 , the depth data module  206  generates a point cloud (i.e., the point cloud  702 ) based on depth data, wherein the point cloud  702  comprises a set of data points that define surface features of an object depicted in a first data stream. For example, each data point of the point cloud  702  (e.g., data point  706 ) comprises data attributes that include location data as well as depth data that identify a position of the data point in a space. 
     In some example embodiments, the 3D modeling toolkit  124  may provide one or more interfaces to generate training data for a machine learned model. For example, a user of the client device  102  may provide inputs that select one or more data points from among the plurality of data points that make up the point cloud  702 , to apply one or more semantic labels to the one or more data points. As seen in the diagram  700 , the labeled points  704  may be presented in a different color or pattern from the unlabeled points of the point cloud  702 . 
       FIG. 8  is a diagram  800  depicting a 3D model  802  retrieved based on a point cloud  848 , according to certain example embodiments. As seen in the diagram  800 , the point cloud  804  defines a set of surface features of an object (i.e., a face). As discussed in operation  502  of the method  500  depicted in  FIG. 5 , the analysis module  210  accesses the point cloud  804  generated by the depth data module  206  and identifies a plurality of landmarks based on the point cloud  804 . 
     The analysis module  210  determines a classification associated with the landmarks defined by the point cloud  804  and causes the 3D model module  208  to retrieve a 3D model  802  from a collection of 3D models, based on at least the classification associated with the plurality of landmarks. The 3D model  802  may then be presented at a position among the presentation of the point cloud  804  at the client device  102 . 
     Additional Embodiments 
     Although the functionality of the 3D modelling toolkit  124  has been described in relation to use of point clouds for generating 3D models of objects, other techniques may also be employed. For example, in some embodiments, the 3D modelling toolkit  124  may employ Artificial Intelligence (AI) modeling techniques, such as Mesh Region Based Convolutional Neural Networks (R-CNN), that convert a two-dimensional (2D) image of an object into a 3D representation of the object. In this type of embodiment, a 2D image depicting an object (e.g., photograph) is processed by the 3D modelling toolkit  124  to generate a voxel grid that represents a coarse 3D surface of the object. For example, the voxel grid includes occupancy probability values indicating a probability that each voxel is occupied by the object. The 3D modelling toolkit  124  converts the voxel grid into a triangle mesh representation of the object (e.g., cubified mesh), which is then further refined to generate the 3D model of the object. 
     The resulting 3D model may be used for a variety of use cases, such as manufacturing physical items based on the object. For example, in some embodiments, the described functionality of the 3D modelling toolkit  124  may be used to manufacture personalized dental aligners for adjusting the alignment of a user&#39;s teeth. In this type of embodiment, the 3D modelling toolkit  124  uses a 2D image depicting the a dental arch of a user to generate a corresponding 3D model of the dental arch, which may then be used to manufacture a personalized dental aligner for the user. Manufacturing dental aligners is just one example, however, and is not meant to be limiting. 
       FIG. 11  is a block diagram illustrating components of a 3D modeling toolkit  124  that configure the 3D modeling toolkit  124  to generate a 3D model based on a voxel grid, according to certain example embodiments. The 3D modeling toolkit  124  is shown as including an image module  1102 , an object detection module  1104 , an object classification module  1106 , a voxel grid generation module  1108 , and a 3D model generation module  1110 , 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 processors  1112  (e.g., by configuring such one or more processors  1112  to perform functions described for that module) and hence may include one or more of the processors  1112 . 
     The image module  1102  accesses images and/or image data used to generate a 3D model of an object. The image module  1102  may access an image from a client device  102  or from a database  120 . For example, a user may use a client application  104  executing on the client device  102  to submit/transmit images to the 3D modeling toolkit  124 , which are accessed by the image module  1102 . As another example, images received from one or more sources may be stored in a database  120  and the image module  1102  may communicate with a database server  118  to access the images. 
     An image accessed by the image module  1102  may depict a physical object. For example, the image may depict a physical object, such an automobile, person, and the like. In some embodiments, the image may depict a dental arch of a human user. A dental arch is a set of teeth, such as a top set of teeth or bottom set of teeth. 
     The image module  1102  may provide an accessed image or images to the other modules of the 3D modelling toolkit  124  for purposes of generating a 3D model of the object depicted in the image. For example, the image module  1102  may provide the image to the object detection module  1104 . 
     The object detection module  1104  detect individual objects depicted in the image or images. For example, an image may depict multiple physical objects, such as multiple people, vehicles, and the like. As another example, an image may depict a physical object that consists of multiple individual physical objects. For example, a physical object such as a vehicle consists of individual physical objects such as tires, doors, windshield, frame, and the like. Similarly, a physical object such as a dental arch consists of multiple individual teeth. 
     The object detection module  1104  may detect the individual objects using any of a variety of object recognition techniques. For example, the object detection module  1104  may utilize an object recognition technique such as Selective Search that is employed by a voxel branch of a mesh R-CNN, to identify the objects in the image. Selective Search generates a hierarchy of successively larger regions that are recursively combined based on similarity to identify regions in the image that depict individual objects. 
     The object detection module  1104  may also use other object recognition techniques, such machine learning model trained based on labeled images of the object and retrained on false positives generated by the model. For example, images of a dental arch that are labeled to identify the individual teeth in the dental arch may be used to train an object recognition machine learning model to identify individual teeth from an image. 
     In any case, the object detection module  1104  generates a set of regions of the image that are determined to depict individual objects or groups of objects. For example, each region may depict one or more individual teeth in a dental arch. The object detection module  1104  may provide data identifying the regions to the other modules of the 3D modelling toolkit  124 . For example, the object detection module  1104  may provide data identifying the regions to the object classification module  1106 . 
     The object classification module  1106  assigns labels to each identified region of an image that identify the object depicted in the region of the image. For example, the object classification module  1106  may assign a label to a region of an image indicating that the region depicts a tire. As another example the object classification module  1106  may assign a label to a region of an image indicating that the region depicts a tooth, group of teeth, or a specific tooth, such as by identifying the specific tooth number (e.g., T1, T2, etc.). 
     The object classification module  1106  initially generates features defining the object depicted in each region. For example, the object classification module  1106  may use a convolutional neural network, such as employed by a voxel branch of a mesh R-CNN, to generate features for each of the respective regions of the image. 
     The object classification module  1106  assigns labels to each region based on the feature data generated for the regions of the image. For example, the object classification module  1106  may use the feature data describing the object in a particular region to assign a label to the region. To accomplish this, the object classification module  1106  may generate an input based on the feature data describing the object in the region, which is then provided as input into a machine learning model, such as a classification model, that provides probability values for a set of classification labels. Each probability value indicates the likelihood that a corresponding classification label properly classifies the object depicted in the region. The object classification module  1106  selects the classification label with the highest probability value to assign to the region of the image. 
     The voxel grid generation module  1108  generates a voxel grid based on the set of features describing the objects in each region of the image. A voxel grid is a 3D grid of voxels representing a region of the image. Each voxel in the voxel grid includes an occupancy probability value that represent a coarse 3D surface of the object depicted in the region of the image. For example, each occupancy probability value indicates the probability that its corresponding voxel is occupied by the object. 
     To generate the voxel grid, the voxel grid generation module  1108  provides the features included in each identified region of the image as input into a machine learning model, such as employed by a voxel branch of a mesh R-CNN, that generates the voxel grid and the occupancy probability values corresponding to each voxel. In some embodiments, the voxel grid generation module  1108  uses the featured data describing each identified object in the image as a singular input into the machine learning model to generate the voxel grid. In other embodiments, the voxel grid generation module  1108  generates separate inputs based on the featured data describing each individual identified object in the image, which are then separately input into machine learning models to generate the voxel grid. For example, an input generated based on features in a portion of the image may be provided to a specific a machine learning model trained specifically based on the type of object depicted in the portion of the image. Alternatively, a combination of these two approaches may be used. 
     The voxel grid generation module  1108  may generate the shape of the voxel grid based on a camera projection matrix. This allows for generation of an irregular shaped voxel grid that accounts for the depth of the object depicted in the image. 
     The voxel grid generation module  1108  uses the voxel grid to generate a triangle mesh representing a 3D surface of the object. For example, the voxel grid generation module  1108  generates a triangle mesh by merging shared vertices and edges between adjacent occupied voxels. 
     The voxel grid generation module  1108  provides the resulting triangle mesh to the 3D model generation module  1110 , which generates a refined 3D model of the object. For example, the 3D model generation module  1110  iteratively processes the triangle mesh of the physical object through a mesh refinement branch of a mesh R-CNN. The mesh refinement branch utilizes three separate stages to refine the triangle mesh. For example, the mesh refinement branch includes a vertex alignment stage, graph convolution stage, and a vertex refinement stage. The vertex alignment stage extracts image features for vertices included in the triangle mesh, the graph convolution stage propagates information along mesh edges, and the vertex refinement stage updates vertex positions. The 3D model generation module  1110  may iteratively repeat these three stages until a suitable refinement mesh representation of the object is achieved. 
       FIG. 12  is a flowchart illustrating a method  1200  for generating a 3D model of an object based on a voxel grid, according to certain example embodiments. Operations of the method  1200  may be performed by the modules described above with respect to  FIG. 11 . 
     At operation  1202 , the image module  1102  accesses an image (e.g., one or more images) depicting an object. The image module  1102  accesses images and/or images data used to generate a 3D model of an object. The image module  1102  may access an image from a client device  102  or from a database  120 . For example, a user may use a client application  104  executing on the client device  102  to submit/transmit images to the 3D modeling toolkit  124 , which are accessed by the image module  1102 . As another example, images received from one or more sources may be stored in a database  120  and the image module  1102  may communicate with a database server  118  to access the images. 
     An image accessed by the image module  1102  may depict a physical object. For example, the image may depict a physical object, such an automobile, person, and the like. In some embodiments, the image may depict a dental arch of a human user. A dental arch is a set of teeth, such as a top set of teeth or bottom set of teeth. 
     The image module  1102  may provide an accessed image to the other modules of the 3D modelling toolkit  124  for purposes of generating a 3D model of the object depicted in the image. For example, the image module  1102  may provide the image to the object detection module  1104 . 
     At operation  1204 , the object classification module  1106  identifies, from the image, a set of features describing the object. For example, the object classification module  1106  may use a convolutional neural network, such as employed by a voxel branch of a mesh R-CNN, to generate the set of features. 
     At operation  1206 , the voxel grid generation module  1108  generates, based on the set of features, a voxel grid representing a 3D surface of the object. To generate the voxel grid, the voxel grid generation module  1108  provides the features describing the object depicted in the image as input into a machine learning model, such as employed by a voxel branch of a mesh R-CNN, that generates the voxel grid and occupancy probability values corresponding to each voxel. 
     At operation  1208 , the 3D model generation module  1110  generates a 3D model of the object based on the voxel grid. For example, the 3D model generation module  1110  iteratively processes a triangle mesh of the physical object through a mesh refinement branch of a mesh R-CNN. The triangle mesh may be generated based on the voxel grid by merging shared vertices and edges between adjacent occupied voxels. 
       FIG. 13  is a flowchart illustrating a method  1300  for generating a 3D model of a dental arch based on a voxel grid, according to certain example embodiments. Operations of the method  1300  may be performed by the modules described above with respect to  FIG. 11 . 
     At operation  1302 , the image module  1102  accesses an image (e.g., one or more images) depicting a dental arch of a user. The dental arch of a user is a row of individual teeth, either top or bottom. The image module  1102  accesses the image from a client device  102  or from a database  120 . For example, a user may use a client application  104  executing on the client device  102  to submit/transmit the image to the 3D modeling toolkit  124 , which are accessed by the image module  1102 . As another example, images received from one or more sources may be stored in a database  120  and the image module  1102  may communicate with a database server  118  to access the images. 
     The image module  1102  may provide the image to the other modules of the 3D modelling toolkit  124  for purposes of generating a 3D model of the dental arch depicted in the image. For example, the image module  1102  may provide the image to the object detection module  1104 . 
     At operation  1304 , the object classification module  1106  identifies, from the image, a set of features describing the dental arch of the user. For example, the object classification module  1106  may use a convolutional neural network, such as employed by a voxel branch of a mesh R-CNN, to generate the set of features. 
     At operation  1206 , the voxel grid generation module  1108  generates, based on the set of features, a voxel grid representing a 3D surface of the dental arch of the user. To generate the voxel grid, the voxel grid generation module  1108  provides the features describing the dental arch (e.g., individual teeth, group of teeth, complete row of teeth) depicted in the image as input into a machine learning model, such as employed by a voxel branch of a mesh R-CNN. The machine learning model in turn generates the voxel grid and occupancy probability values corresponding to each voxel. The voxel grid represents a 3D surface of the dental arch of the user. 
     In some embodiments, the voxel grid generation module  1108  uses the featured data describing the entire dental arch (e.g., all of the teeth) in the image as a singular input into the machine learning model to generate the voxel grid. In other embodiments, the voxel grid generation module  1108  generates separate inputs based on the featured data describing each individual tooth or groups of teeth identified in the image, which are then separately input into machine learning models to generate the voxel grid. For example, an input generated based on features describing a tooth or group of teeth identified in the image may be provided to a specific a machine learning model trained specifically based on the determined type of tooth or group of teeth. Alternatively, a combination of these two approaches may be used. For example, one voxel grid may be generated using the entire dental arch as input into a machine learning model and a second voxel grid may be generated using the individual teeth as input into separate machine learning models. The two voxel grids may then be combined to generate a merged voxel grid representing the 3D surface of the dental arch of the user. 
     At operation  1308 , the 3D model generation module  1110  generates a 3D model of the dental arch of the user based on the voxel grid. For example, the 3D model generation module  1110  iteratively processes a triangle mesh of the dental arch through a mesh refinement branch of a mesh R-CNN. The triangle mesh may be generated based on the voxel grid by merging shared vertices and edges between adjacent occupied voxels. 
     The 3D model of the dental arch may be used for a variety of purposes. For example, in some embodiments, the 3D model of the dental arch may be used to generate/manufacture a dental retainer or dental aligner that is customized to the user. The dental retainer may be designed to maintain a current alignment of the teeth included in the dental arch of the user. In contrast, the dental aligner may be designed to adjust the alignment of one or more of the teeth of included in the dental arch of the user. For example, the dental aligner may be used to adjust the alignment of the teeth to correct crooked or misplaced teeth. 
       FIG. 14  is a flowchart illustrating a method  1400  for identifying teeth depicted in an image, according to certain example embodiments. Operations of the method  1400  may be performed by the modules described above with respect to  FIG. 11 . 
     At operation  1402 , the object detection module  1104  identifies regions of an image that depict one or more individual teeth included in a dental arch. The object detection module  1104  may detect the one or more individual teeth using any of a variety of object recognition techniques. For example, the object detection module  1104  may utilize an object recognition technique such as Selective Search that is employed by a voxel branch of a mesh R-CNN, to identify the teeth in the image. Selective Search generates a hierarchy of successively larger regions that are recursively combined based on similarity to identify regions in the image that depict one or more individual teeth. 
     At operation  1404 , the object classification module  1106  determines features describing the one or more individual teeth depicted in each region of the image. For example, the object classification module  1106  may use a convolutional neural network, such as employed by a voxel branch of a mesh R-CNN, to generate the set of features. 
     At operation  1406 , the object classification module  1106  assigns labels to each region of the image defining a type of the one or more individual teeth depicted in the region. The object classification module  1106  assigns labels to each region based on the feature data generated for the regions of the image. For example, the object classification module  1106  may use the feature data describing the one or more teeth in a particular region to assign a label to the region. To accomplish this, the object classification module  1106  may generate an input based on the feature data describing the one or more teeth in the region. The object classification module  1106  provides the input into a machine learning model, such as a classification model, that provides probability values for a set of classification labels that correspond to the different tooth types. Each probability value indicates the likelihood that a corresponding classification label properly classifies the type of the one or more teeth depicted in the region. The object classification module  1106  selects the classification label with the highest probability value to assign to the region of the image. 
       FIG. 15  is a flowchart illustrating a method  1500  for generating a 3D model of a dental arch from a voxel grid, according to certain example embodiments. Operations of the method  1500  may be performed by the modules described above with respect to  FIG. 11 . 
     At operation  1502 , the voxel grid generation module  1108  generates a voxel grid representing a 3D surface of a dental arch of a user. A voxel grid is a 3D grid of voxels representing the dental arch of the user. Each voxel in the voxel grid includes an occupancy probability value that represent a coarse 3D surface of the dental arch depicted in the image. For example, each occupancy probability value indicates the probability that its corresponding voxel is occupied by the dental arch. 
     At operation  1504 , the voxel grid generation module  1108  generates a triangle mesh of the dental arch based on the voxel grid. For example, the voxel grid generation module  1108  generates a triangle mesh by merging shared vertices and edges between adjacent occupied voxels. 
     At operation  1506 , the 3D model generation module  1110  iteratively processes the triangle mesh through a mesh refinement process. For example, the 3D model generation module  1110  iteratively processes the triangle mesh of the physical object through a mesh refinement branch of a mesh R-CNN. The mesh refinement branch utilizes three separate stages to refine the triangle mesh. For example, the mesh refinement branch includes a vertex alignment stage, graph convolution stage, and a vertex refinement stage. The vertex alignment stage extracts image features for vertices included in the triangle mesh, the graph convolution stage propagates information along mesh edges, and the vertex refinement stage updates vertex positions. The 3D model generation module  1110  may iteratively repeat these three stages until a suitable refinement mesh representation of the object is achieved. 
     Software Architecture 
       FIG. 9  is a block diagram illustrating an example software architecture  906 , 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. The software architecture  906  may execute on hardware such as the machine  900  of  FIG. 9  that includes, among other things, processors  904 , memory  914 , and I/O components  918 . A representative hardware layer  952  is illustrated and can represent, for example, the machine  1000  of  FIG. 10 . The representative hardware layer  952  includes a processing unit  954  having associated executable instructions  904 . Executable instructions  904  represent the executable instructions of the software architecture  906 , including implementation of the methods, components and so forth described herein. The hardware layer  952  also includes memory and/or storage modules memory/storage  956 , which also have executable instructions  904 . The hardware layer  952  may also comprise other hardware  958 . 
     In the example architecture of  FIG. 9 , the software architecture  906  may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software architecture  906  may include layers such as an operating system  902 , libraries  920 , applications  916  and a presentation layer  914 . Operationally, the applications  916  and/or other components within the layers may invoke application programming interface (API) API calls  908  through the software stack and receive a response as in response to the API calls  908 . 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  918 , while others may provide such a layer. Other software architectures may include additional or different layers. 
     The operating system  902  may manage hardware resources and provide common services. The operating system  902  may include, for example, a kernel  922 , services  924  and drivers  926 . The kernel  922  may act as an abstraction layer between the hardware and the other software layers. For example, the kernel  922  may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services  924  may provide other common services for the other software layers. The drivers  926  are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  926  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  920  provide a common infrastructure that is used by the applications  916  and/or other components and/or layers. The libraries  920  provide functionality that allows other software components to perform tasks in an easier fashion than to interface directly with the underlying operating system  902  functionality (e.g., kernel  922 , services  924  and/or drivers  926 ). The libraries  920  may include system libraries  944  (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  920  may include API libraries  946  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. The libraries  920  may also include a wide variety of other libraries  948  to provide many other APIs to the applications  916  and other software components/modules. 
     The frameworks/middleware  918  (also sometimes referred to as middleware) provide a higher-level common infrastructure that may be used by the applications  916  and/or other software components/modules. For example, the frameworks/middleware  918  may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware  918  may provide a broad spectrum of other APIs that may be utilized by the applications  916  and/or other software components/modules, some of which may be specific to a particular operating system  902  or platform. 
     The applications  916  include built-in applications  938  and/or third-party applications  940 . Examples of representative built-in applications  938  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 applications  940  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  940  may invoke the API calls  908  provided by the mobile operating system (such as operating system  902 ) to facilitate functionality described herein. 
     The applications  916  may use built in operating system functions (e.g., kernel  922 , services  924  and/or drivers  926 ), libraries  920 , and frameworks/middleware  918  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 presentation layer  914 . 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 a machine  1000 , 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 the machine  1000  in the example form of a computer system, within which instructions  1010  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1000  to perform any one or more of the methodologies discussed herein may be executed. As such, the instructions  1010  may be used to implement modules or components described herein. The instructions  1010  transform the general, non-programmed machine  1000  into a particular machine  1000  programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  1000  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1000  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  1000  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 the instructions  1010 , sequentially or otherwise, that specify actions to be taken by machine  1000 . Further, while only a single machine  1000  is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions  1010  to perform any one or more of the methodologies discussed herein. 
     The machine  1000  may include processors  1004 , memory memory/storage  1006 , and I/O components  1018 , which may be configured to communicate with each other such as via a bus  1002 . The memory/storage  1006  may include a memory  1014 , such as a main memory, or other memory storage, and a storage unit  1016 , both accessible to the processors  1004  such as via the bus  1002 . The storage unit  1016  and memory  1014  store the instructions  1010  embodying any one or more of the methodologies or functions described herein. The instructions  1010  may also reside, completely or partially, within the memory  1014 , within the storage unit  1016 , within at least one of the processors  1004  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1000 . Accordingly, the memory  1014 , the storage unit  1016 , and the memory of processors  1004  are examples of machine-readable media. 
     The I/O components  1018  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  1 /components  1018  that are included in a particular machine  1000  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  1018  may include many other components that are not shown in  FIG. 10 . The I/O components  1018  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  1018  may include output components  1026  and input components  1028 . The output components  1026  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  1028  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 components  1018  may include biometric components  1030 , motion components  1034 , environmental environment components  1036 , or position components  1038  among a wide array of other components. For example, the biometric components  1030  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  1034  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environment components  1036  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. The position components  1038  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 components  1018  may include communication components  1040  operable to couple the machine  1000  to a network  1032  or devices  1020  via coupling  1022  and coupling  1024  respectively. For example, the communication components  1040  may include a network interface component or other suitable device to interface with the network  1032 . In further examples, communication components  1040  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  1020  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, the communication components  1040  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1040  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 the communication components  1040 , 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 (1×RTT), 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 (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 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. 
     “3D RECONSTRUCTION” in this context refers to a process of building a 3D model using multiple pieces of partial information about a subject. 
     “3D SCAN” in this context refers to the result of a 3D reconstruction. 
     “SIMULTANEOUS LOCATION AND MAPPING (SLAM)” in this context refers to a method of building a map or model of an unknown scene or subject while simultaneously keeping track of a device position within an environment. 
     “DEPTH FRAME” in this context refers to a snapshot in time of depth values from a sensor, arranged in a 2D grid, like an RGB camera frame. In certain embodiments the depth values are the distance in meters from a device to a subject. 
     “POINT CLOUD” in this context refers to and unordered array of points in 3D, wherein each point has an XYZ position, a color, a normal (which is a vector indicating the point&#39;s orientation), and other information. 
     “MESH” in this context refers to a collection of triangles.