PATENT DOCUMENT

Publication Number: US-11361501-B2
Application Number: US-202117158227-A
Country: US
Kind Code: B2

Title: Semantic labeling of point cloud clusters

Abstract:
In one implementation, a method of semantically labeling a point cloud cluster is performed at a device including one or more processors and non-transitory memory. The method includes obtaining a point cloud of a physical environment including a plurality of points, each of the plurality of points associated with coordinates in a three-dimensional space. The method includes spatially disambiguating portions of the plurality of points into a plurality of clusters. The method includes determining a semantic label based on a volumetric arrangement of the points of a particular cluster of the plurality of clusters. The method includes generating a characterization vector of a particular point of the points of the particular cluster, wherein the characterization vector includes the coordinates of the particular point, a cluster identifier of the particular cluster, and the semantic label.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at an electronic device including one or more processors and non-transitory memory: 
 obtaining a point cloud of a physical environment including a plurality of points, each of the plurality of points associated with coordinates in a three-dimensional space; 
 spatially disambiguating portions of the plurality of points into a plurality of clusters; 
 determining a semantic label based on a volumetric arrangement of the points of a particular cluster of the plurality of clusters; and 
 generating a characterization vector of a particular point of the points of the particular cluster, wherein the characterization vector includes the coordinates of the particular point, a cluster identifier of the particular cluster, and the semantic label. 
 
     
     
       2. The method of  claim 1 , wherein obtaining the point cloud includes:
 obtaining a plurality of images of the physical environment from a plurality of different perspectives; and 
 generating the point cloud based on the plurality of images of the physical environment. 
 
     
     
       3. The method of  claim 1 , wherein obtaining the point cloud includes:
 obtaining an image of the physical environment; 
 obtaining a depth map of the image of the physical environment; and 
 generating the point cloud based on the image of the physical environment and the depth map. 
 
     
     
       4. The method of  claim 1 , wherein spatially disambiguating the portions of the plurality of points into the plurality of clusters includes performing plane model segmentation. 
     
     
       5. The method of  claim 1 , wherein spatially disambiguating the portions of the plurality of points into the plurality of clusters includes performing Euclidean cluster extraction. 
     
     
       6. The method of  claim 1 , wherein determining the semantic label includes determining a flatness of the particular cluster. 
     
     
       7. The method of  claim 1 , wherein determining the semantic label includes providing the coordinates of the points of the particular cluster to a machine-learning object classifier to obtain the semantic label. 
     
     
       8. The method of  claim 1 , wherein the characterization vector further includes a color or a color variation. 
     
     
       9. The method of  claim 1 , further comprising:
 spatially disambiguating portions of the particular cluster into a plurality of sub-clusters; 
 determining a semantic sub-label based on the volumetric arrangement of the points of a particular sub-cluster of the plurality of sub-clusters; and 
 generating a characterization vector of a particular point of the points of the particular sub-cluster, wherein the characterization vector includes the coordinates of the particular point, the cluster identifier of the particular cluster, the semantic label, and the semantic sub-label. 
 
     
     
       10. A device comprising:
 a non-transitory memory; and 
 one or more processors to:
 obtain a point cloud of a physical environment including a plurality of points, each of the plurality of points associated with coordinates in a three-dimensional space; 
 spatially disambiguate portions of the plurality of points into a plurality of clusters; 
 determine a semantic label based on a volumetric arrangement of the points of a particular cluster of the plurality of clusters; and 
 generate a characterization vector of a particular point of the points of the particular cluster, wherein the characterization vector includes the coordinates of the particular point, a cluster identifier of the particular cluster, and the semantic label. 
 
 
     
     
       11. The device of  claim 10 , wherein the one or more processors are to obtain the point cloud by:
 obtaining a plurality of images of the physical environment from a plurality of different perspectives; and 
 generating the point cloud based on the plurality of images of the physical environment. 
 
     
     
       12. The device of  claim 10 , wherein the one or more processors are to obtain the point cloud by:
 obtaining an image of the physical environment; 
 obtaining a depth map of the image of the physical environment; and 
 generating the point cloud based on the image of the physical environment and the depth map. 
 
     
     
       13. The device of  claim 10 , wherein the one or more processors are to spatially disambiguate the portions of the plurality of points into the plurality of clusters by performing plane model segmentation. 
     
     
       14. The device of  claim 10 , wherein the one or more processors are to spatially disambiguate the portions of the plurality of points into the plurality of clusters by performing Euclidean cluster extraction. 
     
     
       15. The device of  claim 10 , wherein the one or more processors are to determine the semantic label by determining a flatness of the particular cluster. 
     
     
       16. The device of  claim 10 , wherein the one or more processors are to determine the semantic label by providing the coordinates of the points of the particular cluster to a machine-learning object classifier to obtain the semantic label. 
     
     
       17. The device of  claim 10 , wherein the characterization vector further includes a color or a color variation. 
     
     
       18. The device of  claim 10 , wherein the one or more processors are further to:
 spatially disambiguate portions of the particular cluster into a plurality of sub-clusters; 
 determine a semantic sub-label based on the volumetric arrangement of the points of a particular sub-cluster of the plurality of sub-clusters; and 
 generate a characterization vector of a particular point of the points of the particular sub-cluster, wherein the characterization vector includes the coordinates of the particular point, the cluster identifier of the particular cluster, the semantic label, and the semantic sub-label. 
 
     
     
       19. A non-transitory memory having one or more instructions encoded thereon, which, when executed by one or more processors of a device, cause the device to:
 obtain a point cloud of a physical environment including a plurality of points, each of the plurality of points associated with coordinates in a three-dimensional space; 
 spatially disambiguate portions of the plurality of points into a plurality of clusters; 
 determine a semantic label based on a volumetric arrangement of the points of a particular cluster of the plurality of clusters; and 
 generate a characterization vector of a particular point of the points of the particular cluster, wherein the characterization vector includes the coordinates of the particular point, a cluster identifier of the particular cluster, and the semantic label. 
 
     
     
       20. The non-transitory memory of  claim 19 , wherein the instructions, when executed, cause the device to determine the semantic label by providing the coordinates of the points of the particular cluster to a machine-learning object classifier to obtain the semantic label.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Patent App. No. 62/982,324, filed on Feb. 27, 2020, and hereby incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to point clouds and, in particular, to systems, methods, and devices for semantic labeling of point cloud clusters. 
     BACKGROUND 
     A point cloud includes a set of points in a three-dimensional space. In various implementations, each point in the point cloud corresponds to a surface of an object in a physical environment. Point clouds can be used to represent a physical environment in various computer vision and/or extended reality (XR) applications. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the present disclosure can be understood by those of ordinary skill in the art, a more detailed description may be had by reference to aspects of some illustrative implementations, some of which are shown in the accompanying drawings. 
         FIG. 1  illustrates a physical environment with a handheld electronic device surveying the physical environment. 
         FIGS. 2A and 2B  illustrate the handheld electronic device of  FIG. 1  displaying two images of the physical environment captured from different perspectives. 
         FIGS. 3A and 3B  illustrate the handheld electronic device of  FIG. 1  displaying the two images overlaid with a representation of a point cloud. 
         FIGS. 4A and 4B  illustrate the handheld electronic device of  FIG. 1  displaying the two images overlaid with a representation of the point cloud spatially disambiguated into a plurality of clusters. 
         FIG. 5  illustrates a point cloud data object in accordance with some implementations. 
         FIG. 6  is a flowchart representation of a method of semantically labeling a point cloud in accordance with some implementations. 
         FIG. 7  is a block diagram of an electronic device in accordance with some implementations. 
     
    
    
     In accordance with common practice the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may not depict all of the components of a given system, method or device. Finally, like reference numerals may be used to denote like features throughout the specification and figures. 
     SUMMARY 
     Various implementations disclosed herein include devices, systems, and methods for semantically labeling a point cloud. In various implementations, a method is performed at a device including one or more processors and non-transitory memory. The method includes obtaining a point cloud of a physical environment including a plurality of points, each of the plurality of points associated with a set of coordinates in a three-dimensional space. The method includes spatially disambiguating portions of the plurality of points into a plurality of clusters. The method includes determining a semantic label based on a volumetric arrangement of the points of a particular cluster of the plurality of clusters. The method includes generating a characterization vector of a particular point of the points of the particular cluster, wherein the characterization vector includes the coordinates of the particular point, a cluster identifier of the particular cluster, and the semantic label. 
     In accordance with some implementations, a device includes one or more processors, a non-transitory memory, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors. The one or more programs include instructions for performing or causing performance of any of the methods described herein. In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions, which, when executed by one or more processors of a device, cause the device to perform or cause performance of any of the methods described herein. In accordance with some implementations, a device includes: one or more processors, a non-transitory memory, and means for performing or causing performance of any of the methods described herein. 
     DESCRIPTION 
     A physical environment refers to a physical place that people can sense and/or interact with without aid of electronic devices. The physical environment may include physical features such as a physical surface or a physical object. For example, the physical environment corresponds to a physical park that includes physical trees, physical buildings, and physical people. People can directly sense and/or interact with the physical environment such as through sight, touch, hearing, taste, and smell. In contrast, an extended reality (XR) environment refers to a wholly or partially simulated environment that people sense and/or interact with via an electronic device. For example, the XR environment may include augmented reality (AR) content, mixed reality (MR) content, virtual reality (VR) content, and/or the like. With an XR system, a subset of a person&#39;s physical motions, or representations thereof, are tracked, and, in response, one or more characteristics of one or more virtual objects simulated in the XR environment are adjusted in a manner that comports with at least one law of physics. As an example, the XR system may detect movement of the electronic device presenting the XR environment (e.g., a mobile phone, a tablet, a laptop, a head-mounted device, and/or the like) and, in response, adjust graphical content and an acoustic field presented by the electronic device to the person in a manner similar to how such views and sounds would change in a physical environment. In some situations (e.g., for accessibility reasons), the XR system may adjust characteristic(s) of graphical content in the XR environment in response to representations of physical motions (e.g., vocal commands). 
     There are many different types of electronic systems that enable a person to sense and/or interact with various XR environments. Examples include head-mountable systems, projection-based systems, heads-up displays (HUDs), vehicle windshields having integrated display capability, windows having integrated display capability, displays formed as lenses designed to be placed on a person&#39;s eyes (e.g., similar to contact lenses), headphones/earphones, speaker arrays, input systems (e.g., wearable or handheld controllers with or without haptic feedback), smartphones, tablets, and desktop/laptop computers. A head-mountable system may have one or more speaker(s) and an integrated opaque display. Alternatively, a head-mountable system may be configured to accept an external opaque display (e.g., a smartphone). The head-mountable system may incorporate one or more imaging sensors to capture images or video of the physical environment, and/or one or more microphones to capture audio of the physical environment. Rather than an opaque display, a head-mountable system may have a transparent or translucent display. The transparent or translucent display may have a medium through which light representative of images is directed to a person&#39;s eyes. The display may utilize digital light projection, OLEDs, LEDs, uLEDs, liquid crystal on silicon, laser scanning light sources, or any combination of these technologies. The medium may be an optical waveguide, a hologram medium, an optical combiner, an optical reflector, or any combination thereof. In some implementations, the transparent or translucent display may be configured to become opaque selectively. Projection-based systems may employ retinal projection technology that projects graphical images onto a person&#39;s retina. Projection systems also may be configured to project virtual objects into the physical environment, for example, as a hologram or on a physical surface. 
     In various implementations, a physical environment is represented by a point cloud. The point cloud includes a plurality of points, each of the plurality of points associated with at least a set of coordinates in the three-dimensional space and corresponding to a surface of an object in a physical environment. In various implementations, each of the plurality of points is further associated with other data representative of the surface of the object in the physical environment, such as RGB data representative of the color of the surface of the object. As described herein, at least one of the plurality of points is further associated with a semantic label that represents an object type (or identity) of the surface of the object. For example, the semantic label may be “tabletop” or “table” or “wall”. 
     Numerous details are described in order to provide a thorough understanding of the example implementations shown in the drawings. However, the drawings merely show some example aspects of the present disclosure and are therefore not to be considered limiting. Those of ordinary skill in the art will appreciate that other effective aspects and/or variants do not include all of the specific details described herein. Moreover, well-known systems, methods, components, devices, and circuits have not been described in exhaustive detail so as not to obscure more pertinent aspects of the example implementations described herein. 
       FIG. 1  illustrates a physical environment  101  with a handheld electronic device  110  surveying the physical environment  101 . The physical environment  101  includes a picture  102  hanging on a wall  103 , a table  105  on the floor  106 , and a cylinder  104  on the table  105 . 
     The handheld electronic device  110  displays, on a display, a representation of the physical environment  111  including a representation of the picture  112  hanging on a representation of the wall  113 , a representation of the table  115  on a representation of the floor  116 , and a representation of the cylinder  114  on the representation of the table  115 . In various implementations, the representation of the physical environment  111  is generated based on an image of the physical environment captured with a scene camera of the handheld electronic device  110  having a field-of-view directed toward the physical environment  101 . 
     In addition to the representations of real objects of the physical environment  101 , the representation of the physical environment  111  includes a virtual object  119  displayed on the representation of the table  115 . 
     In various implementations, the handheld electronic device  110  includes a single scene camera (or single rear-facing camera disposed on an opposite side of the handheld electronic device  110  as the display). In various implementations, the handheld electronic device  110  includes at least two scene cameras (or at least two rear-facing cameras disposed on an opposite side of the handheld electronic device  110  as the display). 
       FIG. 2A  illustrates the handheld electronic device  110  displaying a first image  211 A of the physical environment  101  captured from a first perspective.  FIG. 2B  illustrates the handheld electronic device  110  displaying a second image  211 B of the physical environment  101  captured from a second perspective different from the first perspective. 
     In various implementations, the first image  211 A and the second image  211 B are captured by the same camera at different times (e.g., by the same single scene camera at two different times when the handheld electronic device  110  is moved between the two different times). In various implementations, the first image  211 A and the second image  211 B are captured by different cameras at the same time (e.g., by two scene cameras). 
     Using a plurality of images of the physical environment  101  captured from a plurality of different perspectives, such as the first image  211 A and the second image  211 B, the handheld electronic device  110  generates a point cloud of the physical environment  101 . 
       FIG. 3A  illustrates the handheld electronic device  110  displaying the first image  211 A overlaid with a representation of the point cloud  310 .  FIG. 3B  illustrates the handheld electronic device  110  displaying the second image  211 B overlaid with the representation of the point cloud  310 . 
     The point cloud includes a plurality of points, wherein each of the plurality of points is associated with a set of coordinates in a three-dimensional space. For example, in various implementations, each point is associated with an x-coordinate, a y-coordinate, and a z-coordinate. In various implementations, each point in the point cloud corresponds to a feature in the physical environment  101 , such as a surface of an object in the physical environment  101 . 
     The handheld electronic device  110  spatially disambiguates the point cloud into a plurality of clusters. Accordingly, each of the clusters includes a subset of the points of the point cloud  310 . 
       FIG. 4A  illustrates the handheld electronic device  110  displaying the first image  211 A overlaid with the representation of the point cloud  310  spatially disambiguated into a plurality of clusters  412 - 416 .  FIG. 4B  illustrates the handheld electronic device  110  displaying the second image  211 B overlaid with the representation of the point cloud  310  spatially disambiguated into the plurality of clusters  412 - 416 . The representation of the point cloud  310  includes a first cluster  412  (shown in light gray), a second cluster  413  (shown in black), a third cluster  414  (shown in dark gray), a fourth cluster  415  (shown in white), and a fifth cluster  416  (shown in medium gray). 
     In various implementations, each of the plurality of clusters is assigned a unique cluster identifier. For example, the clusters may be assigned numbers, letters, or other unique labels. 
     For each cluster, the handheld electronic device  110  determines a semantic label. In various implementations, each cluster corresponds to an object in the physical environment  101 . For example, in  FIG. 4A  and  FIG. 4B , the first cluster  412  corresponds to the picture  102 , the second cluster  413  corresponds to the wall  103 , the third cluster  414  corresponds to the cylinder  104 , the fourth cluster  415  corresponds to the table  105 , and the fifth cluster  416  corresponds to the floor  106 . In various implementations, the semantic label indicates an object type or identity of the object. 
     In various implementations, the handheld electronic device  110  stores the semantic label in association with each point of the cluster.  FIG. 5  illustrates a point cloud data object  500  in accordance with some implementations. The point cloud data object  500  includes a plurality of data elements (shown as rows in  FIG. 5 ), wherein each data element is associated with a particular point of a point cloud. The data element for a particular point includes a point identifier field  510  that includes a point identifier of a particular point. As an example, the point identifier may be a unique number. The data element for the particular point includes a coordinate field  520  that includes a set of coordinates in a three-dimensional space of the particular point. The data element for the particular point includes a cluster identifier field  530  that includes an identifier of the cluster into which the particular point is spatially disambiguated. As an example, the cluster identifier may be a letter or number. The data element for the particular point includes a semantic label field  540  that includes a semantic label for the cluster into which the particular point is spatially disambiguated. 
     The semantic labels may be stored in association with the point cloud in other ways. For example, the point cloud may be stored as a set of cluster objects, each cluster object including a cluster identifier for a particular cluster, a semantic label of the particular cluster, and a plurality of sets of coordinates corresponding to the plurality of points spatially disambiguated into the particular cluster. 
     The handheld electronic device  110  can use the semantic labels in a variety of ways. For example, in various implementations, the handheld electronic device  110  can display a virtual object, such as a virtual ball, on the top of a cluster labeled as a “table”, but not on the top of a cluster labeled as a “floor”. In various implementations, the handheld electronic device  110  can display a virtual object, such as a virtual painting, over a cluster labeled as a “picture”, but not over a cluster labeled as a “television”. 
       FIG. 6  is a flowchart representation of a method  600  of semantic labeling a point cloud in accordance with some implementations. In various implementations, the method  600  is performed by a device with one or more processors and non-transitory memory. In some implementations, the method  600  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  600  is performed by a processor executing instructions (e.g., code) stored in a non-transitory computer-readable medium (e.g., a memory). 
     The method  600  begins, in block  610 , with the device obtaining a point cloud of a physical environment including a plurality of points, wherein each of the plurality of points is associated with a set of coordinates in a three-dimensional space. 
     In various implementations, obtaining the point cloud includes obtaining a plurality of images of the physical environment from a plurality of different perspectives and generating the point cloud based on the plurality of images of the physical environment. For example, in various implementations, the device detects the same feature in two or more images of the physical environment and using perspective transform geometry, determines the set of coordinates in the three-dimensional space. In various implementations, the plurality of images is captured by the same camera at different times (e.g., by the same single scene camera of the device at different times when the device is moved between the times). In various implementations, the plurality of images is captured by different cameras at the same time (e.g., by multiple scene cameras of the device). 
     In various implementations, obtaining the point cloud includes obtaining an image of a physical environment, obtaining a depth map of the image of the physical environment, and generating the point cloud based on the image of the physical environment and the depth map. In various implementations, the image is captured by a scene camera of the device and the depth map is generated by a depth sensor of the device. 
     In various implementations, obtaining the point cloud includes using a 3D scanner to generate the point cloud. 
     In various implementations, each point is associated with additional data. In various implementations, each point is associated with a color. In various implementations, each point is associated with a color-variation indicating how the point changes color over time. As an example, such information may be useful in discriminating between a semantic label of a “picture” or a “television”. In various implementations, each point is associated with a confidence indicating a probability that the coordinates in the three-dimensional space of the point is the true location of the corresponding surface of the object in the physical environment. 
     The method  600  continues, in block  620 , with the device spatially disambiguating portions of the plurality of points into a plurality of clusters. Each cluster includes a subset of the plurality of points of the point cloud. In various implementations, particular points of the plurality of points (e.g., those designated as noise) are not included in any of the plurality of clusters. 
     Various point cloud clustering algorithms can be used to spatially disambiguate the point cloud. In various implementations, spatially disambiguating portions of the plurality of points into the plurality of clusters includes performing plane model segmentation. Accordingly, certain clusters of the plurality of clusters correspond to sets of points of the point cloud that lie in the same plane. In various implementations, spatially disambiguating portions of the plurality of points into the plurality of clusters includes performing Euclidean cluster extraction. 
     The method  600  continues, in block  630 , with the device determining a semantic label based on the volumetric arrangement of the points of a particular cluster of the plurality of clusters. In various implementations, the device determines a semantic label for each of the plurality of clusters. 
     In various implementations, the device determines the semantic label by determining a flatness of the particular cluster. For example, if a bounding box surrounding the cluster has a depth in a dimension that is substantially smaller than its height and width, the cluster is “flat.” If the flat cluster is vertical, the device determines a semantic label of “wall”. If the flat cluster is horizontal, the device determines a semantic label of “tabletop”, but if the cluster is the lowest such horizontal cluster, the device determines a semantic label of “floor”. 
     In various implementations, the device determines the semantic label with a neural network. In particular, the device applies a neural network to the sets of coordinates in the three-dimensional space of the points of the particular cluster to generate a semantic label. 
     In various implementations, the neural network includes an interconnected group of nodes. In various implementation, each node includes an artificial neuron that implements a mathematical function in which each input value is weighted according to a set of weights and the sum of the weighted inputs is passed through an activation function, typically a non-linear function such as a sigmoid, piecewise linear function, or step function, to produce an output value. In various implementations, the neural network is trained on training data to set the weights. 
     In various implementations, the neural network includes a deep learning neural network. Accordingly, in some implementations, the neural network includes a plurality of layers (of nodes) between an input layer (of nodes) and an output layer (of nodes). In various implementations, the neural network receives, as inputs, the sets of coordinates in the three-dimensional space of the points of the particular cluster. In various implementations, the neural network provides, as an output, a semantic label for the cluster. 
     As noted above, in various implementations, each point is associated with additional data. In various implementations, the additional data is also provided as an input to the neural network. For example, in various implementations, the color or color variation of each point of the cluster is provided to the neural network. In various implementations, the confidence of each point of the cluster is provided to the neural network. 
     In various implementations, the neural network is trained for a variety of object types. For each object type, training data in the form of point clouds of objects of the object type is provided. More particularly, training data in the form of the sets of coordinates in the three-dimensional space of the points of point cloud are provided. Thus, the neural network is trained with many different point clouds of different tables to train the neural network to classify clusters as a “table”. Similarly, the neural network is trained with many different point clouds of different chairs to train the neural network to classify clusters as a “chair”. 
     In various implementations, the neural network includes a plurality of neural network detectors, each trained for a different object type. Each neural network detector, trained on point clouds of objects of the particular object type, provides, as an output, a probability that a particular cluster corresponds to the particular object type in response to receiving the sets of coordinates in the three-dimensional space of the points of the particular cluster. Thus, in response to receiving the sets of coordinates in the three-dimensional space of the points of a particular cluster, a neural network detector for tables may output a 0.9, a neural network detector for chairs may output a 0.5, and a neural network detector for cylinders may output a 0.2. The semantic label is determined based on the greatest output. 
     In various implementations, the device determines multiple semantic labels for the particular cluster. In various implementations, the device determines a series of hierarchical or layered semantic labels for the particular cluster. For example, the device determines a number of semantic labels that identify the object represented by the cluster with increasing degrees of specificity. For example, the device determines a first semantic label of “flat” for the particular cluster indicating that the cluster has one dimension substantially smaller than the other two. The device then determines a second semantic label of “horizontal” indicating that the flat cluster is horizontal, e.g., like a floor or tabletop rather than vertical like a wall or picture. The device then determines a third semantic label of “floor” indicating that that the flat, horizontal cluster is a floor rather than a table or ceiling. The device then determines a fourth semantic label of “carpet” indicating that the floor is carpeted rather than tile or hardwood floor. 
     In various implementations, the device determines sub-labels associated with sub-clusters of the particular cluster. In various implementations, the method includes spatially disambiguating portions of the particular cluster into a plurality of sub-clusters and determining a semantic sub-label based on the volumetric arrangement of the points of a particular sub-cluster of the plurality of clusters. For example, in various implementations, the device determines a first semantic label of “table” for the particular cluster. After spatially disambiguating the table cluster into a plurality of sub-clusters, a first semantic sub-label of “tabletop” is determined for a first sub-cluster, whereas a second semantic sub-label of “leg” is determined for a second sub-cluster. 
     The method  600  continues, in block  640 , with the device generating a characterization vector of a particular point of the points of the particular cluster. In various implementations, the device generates a characterization vector for each of the points of the particular cluster. The characterization vector includes the coordinates of the particular point, a cluster identifier of the particular cluster, and the semantic label. For example, in  FIG. 5 , each data element corresponds to a characterization vector including the coordinates of the particular point in the coordinate field  520 , a cluster identifier in the cluster identifier field  530 , and a semantic label in the semantic label field  540 . 
     In various implementations, the semantic label is a text string, such as “table” or “floor”. In various implementations, the semantic label is a number, such as object type “3” or object type “7” which correspond to a table or the floor. 
     In various implementations, the characterization vector includes multiple semantic labels. In various implementations, the characterization vector includes various layers of semantic labels. In various implementations, the characterization vector includes one or more semantic sub-labels. 
       FIG. 7  is a block diagram of an electronic device  700  in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations the electronic device  700  includes one or more processing units  702  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices and sensors  706 , one or more communication interfaces  708  (e.g., USB, FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces  710 , one or more XR displays  712 , one or more optional interior- and/or exterior-facing image sensors  714 , a memory  720 , and one or more communication buses  704  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  704  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors  706  include at least one of an inertial measurement unit (IMU), an accelerometer, a gyroscope, a thermometer, one or more physiological sensors (e.g., blood pressure monitor, heart rate monitor, blood oxygen sensor, blood glucose sensor, etc.), one or more microphones, one or more speakers, a haptics engine, one or more depth sensors (e.g., a structured light, a time-of-flight, or the like), and/or the like. 
     In some implementations, the one or more XR displays  712  are configured to present XR content to the user. In some implementations, the one or more XR displays  712  correspond to holographic, digital light processing (DLP), liquid-crystal display (LCD), liquid-crystal on silicon (LCoS), organic light-emitting field-effect transitory (OLET), organic light-emitting diode (OLED), surface-conduction electron-emitter display (SED), field-emission display (FED), quantum-dot light-emitting diode (QD-LED), micro-electro-mechanical system (MEMS), and/or the like display types. In some implementations, the one or more XR displays  712  correspond to diffractive, reflective, polarized, holographic, etc. waveguide displays. For example, the electronic device  700  includes a single XR display. In another example, the electronic device  700  includes an XR display for each eye of the user. In some implementations, the one or more XR displays  712  are capable of presenting AR, MR, and/or VR content. 
     In various implementations, the one or more XR displays  712  are video passthrough displays which display at least a portion of a physical environment as an image captured by a scene camera. In various implementations, the one or more XR displays  712  are optical see-through displays which are at least partially transparent and pass light emitted by or reflected off the physical environment. 
     In some implementations, the one or more image sensors  714  are configured to obtain image data that corresponds to at least a portion of the face of the user that includes the eyes of the user (any may be referred to as an eye-tracking camera). In some implementations, the one or more image sensors  714  are configured to be forward-facing so as to obtain image data that corresponds to the physical environment as would be viewed by the user if the electronic device  700  was not present (and may be referred to as a scene camera). The one or more optional image sensors  714  can include one or more RGB cameras (e.g., with a complimentary metal-oxide-semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor), one or more infrared (IR) cameras, one or more event-based cameras, and/or the like. 
     The memory  720  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory  720  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  720  optionally includes one or more storage devices remotely located from the one or more processing units  702 . The memory  720  comprises a non-transitory computer readable storage medium. In some implementations, the memory  720  or the non-transitory computer readable storage medium of the memory  720  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  730  and an XR presentation module  740 . 
     The operating system  730  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the XR presentation module  740  is configured to present XR content to the user via the one or more XR displays  712 . To that end, in various implementations, the XR presentation module  740  includes a data obtaining unit  742 , a semantic labeling unit  744 , an XR presenting unit  746 , and a data transmitting unit  748 . 
     In some implementations, the data obtaining unit  742  is configured to obtain data (e.g., presentation data, interaction data, sensor data, location data, etc.). The data may be obtained from the one or more processing units  702  or another electronic device. For example, in various implementations, the data obtaining unit  742  obtains a point cloud of a physical environment. To that end, in various implementations, the data obtaining unit  742  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the semantic labeling unit  744  is configured to spatially disambiguate a point cloud into a plurality of clusters and determine a semantic label for at least one of the clusters. To that end, in various implementations, the semantic labeling unit  744  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the XR presenting unit  746  is configured to present XR content via the one or more XR displays  712 . To that end, in various implementations, the XR presenting unit  746  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  748  is configured to transmit data (e.g., presentation data, location data, etc.) to the one or more processing units  702 , the memory  720 , or another electronic device. To that end, in various implementations, the data transmitting unit  748  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  742 , the semantic labeling unit  744 , the XR presenting unit  746 , and the data transmitting unit  748  are shown as residing on a single electronic device  700 , it should be understood that in other implementations, any combination of the data obtaining unit  742 , the semantic labeling unit  744 , the XR presenting unit  746 , and the data transmitting unit  748  may be located in separate computing devices. 
     Moreover,  FIG. 7  is intended more as a functional description of the various features that could be present in a particular implementation as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG. 7  could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one implementation to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular implementation. 
     While various aspects of implementations within the scope of the appended claims are described above, it should be apparent that the various features of implementations described above may be embodied in a wide variety of forms and that any specific structure and/or function described above is merely illustrative. Based on the present disclosure one skilled in the art should appreciate that an aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method may be practiced using any number of the aspects set forth herein. In addition, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to or other than one or more of the aspects set forth herein. 
     It will also be understood that, although the terms “first,” “second,” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first node could be termed a second node, and, similarly, a second node could be termed a first node, which changing the meaning of the description, so long as all occurrences of the “first node” are renamed consistently and all occurrences of the “second node” are renamed consistently. The first node and the second node are both nodes, but they are not the same node. 
     The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

Metadata:
Filing Date: 20210126
Publication Date: 20220614
Grant Date: 20220614
Priority Date: 20200227
Inventors: JOTWANI, PAYAL
Assignee: APPLE INC
CPC Classifications: [{"code": "G06V20/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V20/64", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T17/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F18/23", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F18/214", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06V20/64", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06V20/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T17/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06K9/6256", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06K9/6218", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 74673335