PATENT DOCUMENT

Publication Number: US-10832487-B1
Application Number: US-201916580172-A
Country: US
Kind Code: B1

Title: Depth map generation

Abstract:
In one implementation, a method of generating a depth map is performed by a device including one or more processors, non-transitory memory, and a scene camera. The method includes generating, based on a first image and a second image, a first depth map of the second image. The method includes generating, based on the first depth map of the second image and pixel values of the second image, a second depth map of the second image.

Claims:
What is claimed is: 
     
       1. A method comprising:
 capturing, from a first perspective, a first image of a scene; 
 capturing, from a second perspective different than the first perspective, a second image of the scene, the second image including a plurality of second pixels having a respective plurality of second pixel values; 
 generating, based on the first image and the second image, a first depth map of the second image including, for each of a subset of the plurality of second pixels, a depth of the scene for the respective second pixel; 
 generating, based on the first depth map of the second image and the respective plurality of second pixel values, a second depth map of the second image including, for each of the respective plurality of second pixels, a depth of the scene for the respective second pixel; 
 capturing a third image of the scene, the third image including a plurality of third pixels having a respective plurality of third pixel values; and 
 generating, based on the second depth map of the second image and the third image, a first depth map of the third image including, for each of a subset of the plurality of third pixels, a depth of the scene for the respective third pixel. 
 
     
     
       2. The method of  claim 1 , wherein generating the first depth map of the third image is further based on inertial data generated by an inertial measurement unit. 
     
     
       3. The method of  claim 1 , wherein generating the first depth map of the third image is further based on at least one of the first image or the second image. 
     
     
       4. The method of  claim 1 , wherein the first image, second image, and third image are captured with the same camera at different times. 
     
     
       5. The method of  claim 1  wherein the first image and second image are captured with different cameras at the same time. 
     
     
       6. The method of  claim 1 , wherein generating the second depth map of the second image includes applying a neural network to the first depth map of the second image and the respective plurality of second pixel values. 
     
     
       7. The method of  claim 6 , wherein the neural network includes a deep learning neural network. 
     
     
       8. The method of  claim 1 , wherein generating the second depth map of the second image includes applying jitter to the first depth map of the second image based on a noise model. 
     
     
       9. The method  claim 1 , further comprising:
 generating, based on the first depth map of the third image and the respective plurality of third pixel values, a second depth map of the third image including, for each of the respective plurality of third pixels, a depth of the scene for the respective pixel. 
 
     
     
       10. The method of  claim 9 , further comprising applying a depth-of-field effect to the third image based on the second depth map of the third image. 
     
     
       11. The method of  claim 9 , further comprising detecting an object in the third image based on the second depth map of the third image. 
     
     
       12. A device comprising:
 one or more scene cameras; and 
 one or more processors to:
 capture, from a first perspective using the one or more scene cameras; 
 capture, from a second perspective different than the first perspective using the one or more scene cameras, a second image of the scene, the second image including a plurality of second pixels having a respective plurality of second pixel values; 
 generate, based on the first image and the second image, a first depth map of the second image including, for each of a subset of the plurality of second pixels, a depth of the scene for the respective second pixel; 
 generate, based on the first depth map of the second image and the respective plurality of second pixel values, a second depth map of the second image including, for each of the respective plurality of second pixels, a depth of the scene for the respective second pixel; 
 capture, using the one or more scene cameras, a third image of the scene, the third image including a plurality of third pixels having a respective plurality of third pixel values; and 
 generate, based on the second depth map of the second image and the third image, a first depth map of the third image including, for each of a subset of the plurality of third pixels, a depth of the scene for the respective third pixel. 
 
 
     
     
       13. The device of  claim 12 , further comprising an inertial measurement unit, wherein the one or more processors generate the first depth map of the third image further based on inertial data generated by the inertial measurement unit. 
     
     
       14. The device of  claim 12 , wherein the one or more processors generate the first depth map of the third image further based on at least one of the first image or the second image. 
     
     
       15. The device of  claim 12 , wherein the first image, second image, and third image are captured with the same scene camera of the one or more scene cameras at different times. 
     
     
       16. The device of  claim 12 , wherein the one or more scene cameras include two or more scene cameras, wherein the first image and second image are captured with different ones of the two or more scene cameras at the same time. 
     
     
       17. The device of  claim 12 , wherein the one or more processors generate the second depth map of the second image by applying a neural network to the first depth map of the second image and the respective plurality of second pixel values. 
     
     
       18. The device of  claim 12 , wherein the one or more processors are further to:
 generate, based on the first depth map of the third image and the respective plurality of third pixel values, a second depth map of the third image including, for each of the respective plurality of third pixels, a depth of the scene for the respective pixel; and 
 apply a depth-of-field effect to the third image based on the second depth map of the third image. 
 
     
     
       19. The device of  claim 12 , wherein the one or more processors are further to
 generate, based on the first depth map of the third image and the respective plurality of third pixel values, a second depth map of the third image including, for each of the respective plurality of third pixels, a depth of the scene for the respective pixel; and 
 detect an object in the third image based on the second depth map of the third image. 
 
     
     
       20. A non-transitory computer-readable medium having instructions encoded thereon which, when executed by one or more processors of a device including one or more scene cameras, cause the device to:
 capture, from a first perspective, a first image of a scene; 
 capture, from a second perspective different than the first perspective, a second image of the scene, the second image including a plurality of second pixels having a respective plurality of second pixel values; 
 generate, based on the first image and the second image, a first depth map of the second image including, for each of a subset of the plurality of second pixels, a depth of the scene for the respective second pixel; 
 generate, based on the first depth map of the second image and the respective plurality of second pixel values, a second depth map of the second image including, for each of the respective plurality of second pixels, a depth of the scene for the respective second pixel; 
 capture a third image of the scene, the third image including a plurality of third pixels having a respective plurality of third pixel values; and 
 generate, based on the second depth map of the second image and the third image, a first depth map of the third image including, for each of a subset of the plurality of third pixels, a depth of the scene for the respective third pixel.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Patent App. No. 62/737,433, filed on Sep. 27, 2018, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to generating a depth map, and in particular, to systems, methods, and devices for generating a depth map based on a sparse depth map and an image. 
     BACKGROUND 
     A physical environment refers to a physical world that people can sense and/or interact with without aid of electronic systems. Physical environments, such as a physical park, include physical articles, such as 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, a computer-generated reality (CGR) environment refers to a wholly or partially simulated environment that people sense and/or interact with via an electronic system. In CGR, 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 CGR environment are adjusted in a manner that comports with at least one law of physics. For example, a CGR system may detect a person&#39;s head turning and, in response, adjust graphical content and an acoustic field presented 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), adjustments to characteristic(s) of virtual object(s) in a CGR environment may be made in response to representations of physical motions (e.g., vocal commands). 
     A person may sense and/or interact with a CGR object using any one of their senses, including sight, sound, touch, taste, and smell. For example, a person may sense and/or interact with audio objects that create 3D or spatial audio environment that provides the perception of point audio sources in 3D space. In another example, audio objects may enable audio transparency, which selectively incorporates ambient sounds from the physical environment with or without computer-generated audio. In some CGR environments, a person may sense and/or interact only with audio objects. 
     Examples of CGR include virtual reality and mixed reality. 
     A virtual reality (VR) environment refers to a simulated environment that is designed to be based entirely on computer-generated sensory inputs for one or more senses. A VR environment comprises a plurality of virtual objects with which a person may sense and/or interact. For example, computer-generated imagery of trees, buildings, and avatars representing people are examples of virtual objects. A person may sense and/or interact with virtual objects in the VR environment through a simulation of the person&#39;s presence within the computer-generated environment, and/or through a simulation of a subset of the person&#39;s physical movements within the computer-generated environment. In contrast to a VR environment, which is designed to be based entirely on computer-generated sensory inputs, a mixed reality (MR) environment refers to a simulated environment that is designed to incorporate sensory inputs from the physical environment, or a representation thereof, in addition to including computer-generated sensory inputs (e.g., virtual objects). On a virtuality continuum, a mixed reality environment is anywhere between, but not including, a wholly physical environment at one end and virtual reality environment at the other end. 
     In some MR environments, computer-generated sensory inputs may respond to changes in sensory inputs from the physical environment. Also, some electronic systems for presenting an MR environment may track location and/or orientation with respect to the physical environment to enable virtual objects to interact with real objects (that is, physical articles from the physical environment or representations thereof). For example, a system may account for movements so that a virtual tree appears stationery with respect to the physical ground. 
     Examples of mixed realities include augmented reality and augmented virtuality. 
     An augmented reality (AR) environment refers to a simulated environment in which one or more virtual objects are superimposed over a physical environment, or a representation thereof. For example, an electronic system for presenting an AR environment may have a transparent or translucent display through which a person may directly view the physical environment. The system may be configured to present virtual objects on the transparent or translucent display, so that a person, using the system, perceives the virtual objects superimposed over the physical environment. Alternatively, a system may have an opaque display and one or more imaging sensors that capture images or video of the physical environment, which are representations of the physical environment. The system composites the images or video with virtual objects, and presents the composition on the opaque display. A person, using the system, indirectly views the physical environment by way of the images or video of the physical environment, and perceives the virtual objects superimposed over the physical environment. As used herein, a video of the physical environment shown on an opaque display is called “pass-through video,” meaning a system uses one or more image sensor(s) to capture images of the physical environment, and uses those images in presenting the AR environment on the opaque display. Further alternatively, a system may have a projection system that projects virtual objects into the physical environment, for example, as a hologram or on a physical surface, so that a person, using the system, perceives the virtual objects superimposed over the physical environment. 
     An augmented reality environment also refers to a simulated environment in which a representation of a physical environment is transformed by computer-generated sensory information. For example, in providing pass-through video, a system may transform one or more sensor images to impose a select perspective (e.g., viewpoint) different than the perspective captured by the imaging sensors. As another example, a representation of a physical environment may be transformed by graphically modifying (e.g., enlarging) portions thereof, such that the modified portion may be representative but not photorealistic versions of the originally captured images. As a further example, a representation of a physical environment may be transformed by graphically eliminating or obfuscating portions thereof. 
     An augmented virtuality (AV) environment refers to a simulated environment in which a virtual or computer generated environment incorporates one or more sensory inputs from the physical environment. The sensory inputs may be representations of one or more characteristics of the physical environment. For example, an AV park may have virtual trees and virtual buildings, but people with faces photorealistically reproduced from images taken of physical people. As another example, a virtual object may adopt a shape or color of a physical article imaged by one or more imaging sensors. As a further example, a virtual object may adopt shadows consistent with the position of the sun in the physical environment. 
     There are many different types of electronic systems that enable a person to sense and/or interact with various CGR environments. Examples include head mounted 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 mounted system may have one or more speaker(s) and an integrated opaque display. Alternatively, a head mounted system may be configured to accept an external opaque display (e.g., a smartphone). The head mounted 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 mounted 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 source, 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 one embodiment, 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. 
     To provide a CGR experience based on a physical environment, the distance between a camera imaging the physical environment (referred to as a scene camera) and various objects in the scene may be useful. Various implementations determine the distance from the scene camera to a small number of points in the physical environment in the form of sparse depth map. Accordingly, to improve the CGR experience, various implementations disclosed herein determine the distance to many more points in the physical environment in the form of a dense depth map based on the sparse depth map and an image of the physical environment. 
    
    
     
       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  is a block diagram of an example operating environment in accordance with some implementations. 
         FIG. 2  is a block diagram of an example controller in accordance with some implementations. 
         FIG. 3  is a block diagram of an example HMD in accordance with some implementations. 
         FIG. 4  illustrates a scene with a handheld electronic device surveying the scene. 
         FIG. 5A  illustrates the handheld electronic device of  FIG. 4  displaying a first image of the scene captured from a first perspective. 
         FIG. 5B  illustrates the handheld electronic device of  FIG. 4  displaying a second image of the scene captured from a second perspective different from the first perspective. 
         FIG. 6  illustrates the handheld electronic device of  FIG. 4  displaying a third image of the scene captured from a third perspective. 
         FIG. 7  is a flowchart representation of a method of generating a depth map 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 generating a dense depth map. In various implementations, the method is performed at a device including one or more processors, non-transitory memory, and a scene camera. The method includes capturing, from a first perspective, a first image of a scene and capturing, from a second perspective different than the first perspective, a second image of the scene, the second image including a plurality of second pixels having a respective plurality of second pixel values. The method includes generating, based on the first image and the second image, a first depth map of the second image including, for each of a subset of the plurality of second pixels, a depth of the scene for the respective second pixel. The method includes generating, based on the first depth map of the second image and the respective plurality of second pixel values, a second depth map of the second image including, for each of the respective plurality of second pixels, a depth of the scene for the respective pixel. The method includes capturing a third image of the scene, the third image including a plurality of third pixels having a respective plurality of third pixel values. The method includes generating, based on the second depth map and the third image, a first depth map of the third image including, for each of a subset of the plurality of third pixels, a depth of the scene for the respective third pixel. 
     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 and 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 
     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. 
     In various implementations, a device surveys a scene using a scene camera and generates a depth map indicating the depth from the scene camera to various objects and/or surfaces within the scene represented by an image captured by the scene camera. This depth information can be used in a variety of applications, e.g., to detect real objects in the scene or place virtual objects in the scene. Increasing the accuracy and amount of this depth information improves the user experience of such applications. 
       FIG. 1  is a block diagram of an example operating environment  100  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment  100  includes a controller  110  and an HMD  120 . 
     In some implementations, the controller  110  is configured to manage and coordinate a CGR experience for the user. In some implementations, the controller  110  includes a suitable combination of software, firmware, and/or hardware. The controller  110  is described in greater detail below with respect to  FIG. 2 . In some implementations, the controller  110  is a computing device that is local or remote relative to the scene  105 . For example, the controller  110  is a local server located within the scene  105 . In another example, the controller  110  is a remote server located outside of the scene  105  (e.g., a cloud server, central server, etc.). In various implementations, the scene  105  is a physical environment. In some implementations, the controller  110  is communicatively coupled with the HMD  120  via one or more wired or wireless communication channels  144  (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x, etc.). In another example, the controller  110  is included within the enclosure of HMD  120 . 
     In some implementations, the HMD  120  is configured to provide the CGR experience to the user. In some implementations, the HMD  120  includes a suitable combination of software, firmware, and/or hardware. The HMD  120  is described in greater detail below with respect to  FIG. 3 . In some implementations, the functionalities of the controller  110  are provided by and/or combined with the HMD  120 . 
     According to some implementations, the HMD  120  provides a CGR experience to the user while the user is virtually and/or physically present within the scene  105 . In some implementations, while presenting an AR experience, the HMD  120  is configured to present AR content (e.g., one or more virtual objects) and to enable optical see-through of the scene  105 . In some implementations, while presenting an AR experience, the HMD  120  is configured to present AR content (e.g., one or more virtual objects) overlaid or otherwise combined with images or portions thereof captured by the scene camera of HMD  120 . In some implementations, while presenting AV content, the HMD  120  is configured to present elements of the real world, or representations thereof, combined with or superimposed over a user&#39;s view of a computer-simulated environment. In some implementations, while presenting a VR experience, the HMD  120  is configured to present VR content. 
     In some implementations, the user wears the HMD  120  on his/her head. As such, the HMD  120  includes one or more CGR displays provided to display the CGR content. For example, in various implementations, the HMD  120  encloses the field-of-view of the user. In some implementations, the HMD  120  is replaced with a handheld device (such as a smartphone or tablet) configured to present CGR content, and rather than wearing the HMD  120  the user holds the device with a display directed towards the field-of-view of the user and a camera directed towards the scene  105 . In some implementations, the handheld device can be placed within an enclosure that can be worn on the head of the user. In some implementations, the HMD  120  is replaced with a CGR chamber, enclosure, or room configured to present CGR content in which the user does not wear or hold the HMD  120 . 
       FIG. 2  is a block diagram of an example of the controller  110  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 controller  110  includes one or more processing units  202  (e.g., microprocessors, application-specific integrated-circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), central processing units (CPUs), processing cores, and/or the like), one or more input/output (I/O) devices  206 , one or more communication interfaces  208  (e.g., universal serial bus (USB), FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, global system for mobile communications (GSM), code division multiple access (CDMA), time division multiple access (TDMA), global positioning system (GPS), infrared (IR), BLUETOOTH, ZIGBEE, and/or the like type interface), one or more programming (e.g., I/O) interfaces  210 , a memory  220 , and one or more communication buses  204  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  204  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices  206  include at least one of a keyboard, a mouse, a touchpad, a joystick, one or more microphones, one or more speakers, one or more image sensors, one or more displays, and/or the like. 
     The memory  220  includes high-speed random-access memory, such as dynamic random-access memory (DRAM), static random-access memory (SRAM), double-data-rate random-access memory (DDR RAM), or other random-access solid-state memory devices. In some implementations, the memory  220  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  220  optionally includes one or more storage devices remotely located from the one or more processing units  202 . The memory  220  comprises a non-transitory computer readable storage medium. In some implementations, the memory  220  or the non-transitory computer readable storage medium of the memory  220  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  230  and a CGR experience module  240 . 
     The operating system  230  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR experience module  240  is configured to manage and coordinate one or more CGR experiences for one or more users (e.g., a single CGR experience for one or more users, or multiple CGR experiences for respective groups of one or more users). To that end, in various implementations, the CGR experience module  240  includes a data obtaining unit  242 , a tracking unit  244 , a coordination unit  246 , and a data transmitting unit  248 . 
     In some implementations, the data obtaining unit  242  is configured to obtain data (e.g., presentation data, interaction data, sensor data, location data, etc.) from at least the HMD  120 . To that end, in various implementations, the data obtaining unit  242  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the tracking unit  244  is configured to map the scene  105  and to track the position/location of at least the HMD  120  with respect to the scene  105 . To that end, in various implementations, the tracking unit  244  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the coordination unit  246  is configured to manage and coordinate the CGR experience presented to the user by the HMD  120 . To that end, in various implementations, the coordination unit  246  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  248  is configured to transmit data (e.g., presentation data, location data, etc.) to at least the HMD  120 . To that end, in various implementations, the data transmitting unit  248  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  242 , the tracking unit  244 , the coordination unit  246 , and the data transmitting unit  248  are shown as residing on a single device (e.g., the controller  110 ), it should be understood that in other implementations, any combination of the data obtaining unit  242 , the tracking unit  244 , the coordination unit  246 , and the data transmitting unit  248  may be located in separate computing devices. 
     Moreover,  FIG. 2  is intended more as functional description of the various features that may 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. 2  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. 
       FIG. 3  is a block diagram of an example of the HMD  120  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 HMD  120  includes one or more processing units  302  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices and sensors  306 , one or more communication interfaces  308  (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  310 , one or more CGR displays  312 , one or more optional interior and/or exterior facing image sensors  314 , a memory  320 , and one or more communication buses  304  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  304  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors  306  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 CGR displays  312  are configured to provide the CGR experience to the user. In some implementations, the one or more CGR displays  312  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 CGR displays  312  correspond to diffractive, reflective, polarized, holographic, etc. waveguide displays. For example, the HMD  120  includes a single CGR display. In another example, the HMD  120  includes a CGR display for each eye of the user. In some implementations, the one or more CGR displays  312  are capable of presenting AR and VR content. In some implementations, the one or more CGR displays  312  are capable of presenting AR or VR content. 
     In some implementations, the one or more image sensors  314  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  314  are configured to be forward-facing so as to obtain image data that corresponds to the scene as would be viewed by the user if the HMD  120  was not present (and may be referred to as a scene camera). The one or more optional image sensors  314  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  320  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  320  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  320  optionally includes one or more storage devices remotely located from the one or more processing units  302 . The memory  320  comprises a non-transitory computer readable storage medium. In some implementations, the memory  320  or the non-transitory computer readable storage medium of the memory  320  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  330  and a CGR presentation module  340 . 
     The operating system  330  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR presentation module  340  is configured to present CGR content to the user via the one or more CGR displays  312 . To that end, in various implementations, the CGR presentation module  340  includes a data obtaining unit  342 , a CGR presenting unit  344 , a depth map generating unit  346 , and a data transmitting unit  348 . 
     In some implementations, the data obtaining unit  342  is configured to obtain data (e.g., presentation data, interaction data, sensor data, location data, etc.) from at least the controller  110 . To that end, in various implementations, the data obtaining unit  342  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the CGR presenting unit  344  is configured to present CGR content via the one or more CGR displays  312 . To that end, in various implementations, the CGR presenting unit  344  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the depth map generating unit  346  is configured to generate one or more depth maps of a scene based on one or more images of the scene (e.g., captured using a scene camera of the one or more image sensors  314 ). To that end, in various implementations, the depth map generating unit  346  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  348  is configured to transmit data (e.g., presentation data, location data, etc.) to at least the controller  110 . To that end, in various implementations, the data transmitting unit  348  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  342 , the CGR presenting unit  344 , the depth map generating unit  346 , and the data transmitting unit  348  are shown as residing on a single device (e.g., the HMD  120 ), it should be understood that in other implementations, any combination of the data obtaining unit  342 , the CGR presenting unit  344 , the depth map generating unit  346 , and the data transmitting unit  348  may be located in separate computing devices. 
     Moreover,  FIG. 3  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. 3  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. 
       FIG. 4  illustrates a scene  405  with an electronic device, such as an HMD (e.g., HMD  120 ) or handheld electronic device  410 , surveying the scene  405 . The scene  405  includes a picture  406  hanging on a wall  407  and a table  408 . 
     The handheld electronic device  410  displays, on a display, a representation of the scene  415  including a representation of the picture  416  hanging on a representation of the wall  417  and a representation of the table  418 . In various implementations, the representation of the scene  415  is generated based on an image of the scene captured with a scene camera of the handheld electronic device  410  having a field-of-view directed toward the scene  405 . 
     In various implementations, the handheld electronic device  410  includes a single scene camera (or single rear-facing camera disposed on an opposite side of the handheld electronic device as the display). In various implementations, the handheld electronic device  410  includes at least two scene cameras (or at least two rear-facing cameras disposed on an opposite side of the handheld electronic device as the display). 
       FIG. 5A  illustrates the handheld electronic device  410  displaying a first image  515 A of the scene  405  captured from a first perspective.  FIG. 5B  illustrates the handheld electronic device  410  displaying a second image  515 B of the scene  405  captured from a second perspective different from the first perspective. 
     In various implementations, the first image  515 A and the second image  515 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  410  is moved between the two times). In various implementations, the first image  515 A and the second image  515 B are captured by different cameras at the same time (e.g., by two scene cameras). 
     An image includes a matrix of pixels, each pixel having a corresponding pixel value and a corresponding pixel location. In various implementations, the pixel values range from 0 to 255. In various implementations, each pixel value is a color triplet including three values corresponding to three color channels. For example, in one implementation, an image is an RGB image and each pixel value includes a red value, a green value, and a blue value. As another example, in one implementation, an image is a YUV image and each pixel value includes a luminance value and two chroma values. In various implementations, the image is a YUV444 image in which each chroma value is associated with one pixel. In various implementations, the image is a YUV420 image in which each chroma value is associated with a 2×2 block of pixels (e.g., the chroma values are downsampled). While specific image formats are provided, it should be appreciated that pixel formats may be used. 
     Accordingly, in various implementations, the first image  515 A includes a plurality of first pixels having a respective plurality of first pixel values and the second image  515 B includes a plurality of second pixels have a respective plurality of second pixel values. 
     The handheld electronic device  410 , based on the first image  515 A and second image  515 B, generates a sparse depth map of the second image  515 B using one or more of a variety of techniques. The sparse depth map of the second image  515 B includes, for each of a subset of the plurality of second pixels, a depth of the scene  405  for the respective second pixel. The depth of the scene  405  for a respective second pixel indicates a distance from the scene camera that captured the second image  515 B to an object in the scene represented at the respective second pixel. 
     In various implementations, the handheld electronic device  410  includes two scene cameras which respectively capture the first image  515 A and the second image  515 B. Thus, in various implementations, the sparse depth map of the second image  515 B is generated based on the first image  515 A, the second image  515 B, and stored information concerning the distance between two scene cameras. For example, in various implementations, the handheld electronic device  410  detects a feature in the first image  515 A, such as the corner of the table  418 , at a first pixel location of the first image  515 A and detects the same feature at a second pixel location of the second image  515 B. Based on the stored information concerning the distance between the two scene cameras and the difference between the first pixel location and the second pixel location, the distance from the scene cameras to the feature can be determined using geometric algorithms. 
     In various implementations, the sparse depth map of the second image  515 B is generated based on the first image  515 A, the second image  515 B, and inertial measurement data generated by an inertial measurement unit (IMU) of the handheld electronic device  410 . Accordingly, in various implementations, the sparse depth map of the second image  515 B is generated according to a visual inertial odometry (VIO) algorithm. For example, in various implementations, the handheld electronic device  410  detects a feature in the first image  515 A, such as the corner of the table  418 , at a first pixel location of the first image  515 A and detects the same feature at a second pixel location of the second image  515 B. Based on the motion of the handheld electronic device  410  as indicated by the inertial data and the difference between the first pixel location and the second pixel location, the distance from the scene camera to the feature can be determined using geometric algorithms. 
     Accordingly, in various implementations, the sparse depth map of the second image  515 B includes a depth of the scene  405  for each of a plurality of second pixels at which a corresponding feature is detected. In various implementations, each feature corresponds to a surface of an object in the scene  405 , such as the table  408 , the wall  417 , or the picture  416 . Typically, the number of detected features is much less than the number of pixels of the second image  515 B. For example, in various implementations, the sparse depth map of the second image  515 B includes depths for less than 10% of the second pixels, less than 1% of the second pixels, or less than 0.1% of the second pixels. 
     Thus, in various implementations, the handheld electronic device  410  generates a dense depth map of the second image  515 B based on the sparse depth map of the second image  515 B and the respective plurality of second pixel values of the second image  515 B. In various implementations, the dense depth map of the second image  515 B includes a depth of the scene for each of the second pixels. In various implementations, the dense depth map of the second image  515 B includes a depth of the scene for less than all of the second pixels, but for more pixels than the sparse depth map. For example, in various implementations, the dense depth map of the second image  515 B includes depths for two times, three times, ten times, or a hundred times as many pixel locations as the sparse depth map of the second image  515 B. 
     In various implementations, the dense depth map of the second image  515 B is generated by applying a neural network to the sparse depth map of the second image and the respective plurality of second pixel values. 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 sparse depth map including a depth value for each of a subset of the second pixels and the second pixel values for each of the second pixels. In various implementations, the neural network provides, as outputs, the dense depth map including a depth value for each of the second pixels. 
     In various implementations, jitter is added to the sparse depth map before providing the sparse depth map to the neural network. In various implementations, the jitter added to the sparse depth map is based on a noise model of the sparse depth map based on the generating algorithm. For example, in various implementations, the sparse depth map is generated according to a VIO algorithm that provides, for each depth, a confidence measurement and the amount of jitter added to the depth of the sparse depth map is based on the confidence measurement. 
       FIG. 6  illustrates the handheld electronic device  410  displaying a third image  615 . In various implementations, the third image  615  includes a plurality of third pixels having a respective plurality of third pixel values. In various implementations, the third image  615  is captured (e.g., by a single scene camera of the handheld electronic device  410  or one or multiple scene cameras of the handheld electronic device  410 ) at a later time than the first image  515 A and/or the second image  515 B. 
     In various implementations, the dense depth map of the second image  515 B is fed back for use in generating a sparse depth map of the third image  615 . Accordingly, in various implementations, the handheld electronic device  410  generates, based on the dense depth map of the second image  515 B and the third image  615 , a sparse depth map of the third image  615  including, for each of a subset of the plurality of third pixels, a depth of the scene for the respective third pixel. In various implementations, the sparse depth map of the third image  615  is further based on the first image  515 A and/or the second image  515 B. In various implementations, the sparse depth map of the third image  615  is further based on inertial data generated by the IMU. Thus, in various implementations, the sparse depth map of the third image  615  is generated using a VIO algorithm. 
     In various implementations, the dense depth map of the second image  515 B is used to generate the sparse depth map of the third image  615  in a variety of ways. For example, in various implementations, the dense depth map of the second image  515 B is used to detect features in the third image  615  (and one or more of the first image  515 A and the second image  515 B). In various implementations, the dense depth map of the second image  515 B is used to increase respective confidence measurements associated with respective depth values of the sparse depth map of the third image  615 . In various implementations, the dense depth map of the second image  515 B is used to resolve ambiguities in potential depth values for particular third pixels of the sparse depth map of the third image  615 . In various implementations, the depth values of the dense depth map of the second image  515 B are averaged with (or otherwise incorporated into) depth values of the sparse depth map of the third image  615 . 
     In various implementations, the handheld electronic device  410  further generates a dense depth map of the third image  615  in substantially the same way as the dense depth map of the second image  515 B is generated. For example, in various implementations, the sparse depth map of the third image  615  and the respective third pixel values are input into a neural network (e.g., a deep learning neural network) which outputs the dense depth map of the third image  615 . In various implementations, the dense depth map of the third image  615  includes, for each of the respective plurality of third pixels, a depth of the scene for the respective pixel. 
     In various implementations, the handheld electronic device  410  continues to feedback dense depth maps to generate subsequent sparse depth maps for subsequent images (e.g., a fourth image, a fifth image, a sixth image, etc.) This feedback increases the accuracy of both the sparse depth maps and the dense depth maps for subsequent images. 
     In various implementations, the dense depth map is used to apply a depth-of-field effect to a subsequent image (e.g., the third image) based on the dense depth map of the subsequent image. For example, in various implementations, background pixels (e.g., those with a depth greater than a threshold) are blurred resulting in a portrait effect. As another example, in various implementations, lighting effects can be applied to the subsequent image based on the dense depth map. In various implementations, the dense depth map is used to detect an object in a subsequent image (e.g., the third image) based on the dense depth map. For example, depth-based facial recognition may be performed on the image. 
       FIG. 7  is a flowchart representation of a method  700  of generating a depth map in accordance with some implementations. In various implementations, the method  700  is performed by a device with one or more processors, non-transitory memory, and a camera (e.g., the HMD  120   FIG. 3  or handheld electronic device  410  as described above with respect to  FIGS. 4-6 ). In some implementations, the method  700  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  700  is performed by a processor executing instructions (e.g., code) stored in a non-transitory computer-readable medium (e.g., a memory). Briefly, in some circumstances, the method  700  includes: generating a dense depth map of an image based on pixel values of the image and a sparse depth map of the image and generating a sparse depth map of a subsequent image based on the dense depth map. 
     The method  700  begins, in block  710 , with the device capturing, from a first perspective, a first image of a scene. In various implementations, the first image includes a plurality of first pixels having a respective plurality of first pixel values. For example,  FIG. 5A  illustrates a first image  515 A of a scene  405  from a first perspective. 
     The method  700  continues, in block  720 , with the device capturing, from a second perspective different than the first perspective, a second image of the scene. In various implementations, the second image includes a plurality of second pixels having a respective plurality of second pixel values. For example,  FIG. 5B  illustrates a second image  515 B of a scene  405  from a second perspective. 
     In various implementations, the first image is captured (in block  710 ) and the second image is captured (in block  720 ) with different cameras at the same time. Accordingly, the method  700  can be performed by a device with two (or more) scene cameras, such as a pair of stereoscopic cameras. In various implementations, the first image is captured (in block  710 ) and the second image is captured (in block  720 ) with the same camera at different times. Accordingly, the method  700  can be performed by a device with only a single scene camera (or a single front-facing camera and/or a single rear-facing camera). Thus, pre-existing devices without multiple cameras can perform the method to obtain a high-quality depth map and perform operations based on such a depth map, such as portrait effects or accurate object detection. 
     The method  700  continues, in block  730 , with the device generating, based on the first image and the second image, a first depth map (e.g., a sparse depth map) of the second image including, for each of a subset of the plurality of second pixels, a depth of the scene for the respective second pixel. In various implementations, the first depth map of the second image is further based on inertial measurement data generated by an inertial measurement unit (IMU) of the device. Accordingly, in various implementations, the first depth map of the second image is generated according to a visual inertial odometry (VIO) algorithm. 
     The method  700  continues, in block  740 , with the device generating, based on the first depth map of the second image and the respective plurality of second pixel values, a second depth map (e.g., a dense depth map) of the second image including, for each of the respective plurality of second pixels, a depth of the scene for the respective second pixel. In various implementations, the second depth map of the second image includes a depth of the scene for each second pixel of the second image. In various implementations, the second depth map of the second image includes a depth of the scene for less than all of the second pixels of the second image, but for more pixels than the first depth map. For example, in various implementations, the second depth map of the second image includes depths for two times, three times, ten times, or a hundred times as many pixel locations as the first depth map of the second image. 
     In various implementations, generating the second depth map of the second image includes applying a neural network to the first depth map of the second image and the respective plurality of second pixel values. In various implementations, the neural network includes a deep learning neural network. In various implementations, generating the second depth map of the second image includes applying jitter to the first depth map of the second image based on a noise model. For example, in various implementations, jitter is added to the first depth map of the second image before providing the first depth map of the second image to the neural network. 
     The method  700  continues, in block  750 , with the device capturing a third image of the scene. In various implementations, the third image includes a plurality of third pixels having a respective plurality of third pixel values. For example,  FIG. 6  illustrates a third image  615  of the scene  405  from a third perspective. In various implementations, the first image is captured (in block  710 ), the second image is captured (in block  720 ), and the third image is captured (in block  750 ) with the same camera at different times. 
     The method  700  continues, in block  760 , with the device generating, based on the second depth map of the second image and the third image, a first depth map (e.g., a sparse depth map) of the third image including, for each of a subset of the plurality of third pixels, a depth of the scene for the respective third pixel. In various implementations, generating the first depth map of the third image is further based on at least one of the first image or the second image. In various implementations, generating the first depth map of the third image is further based on inertial data generated by an inertial measurement unit (IMU) of the device. 
     Accordingly, the first depth map of the third image is based on the second depth map of the second image. In various implementations, the second depth map of the second image is used to generate the first depth map of the third image in a variety of ways. For example, in various implementations, the second depth map of the second image is used to detect features in the third image (and one or more of the first image and the second image). In various implementations, the second depth map of the second image is used to increase respective confidence measurements associated with respective depth values of the first depth map of the third image. In various implementations, the second depth map of the second image is used to resolve ambiguities in potential depth values for particular third pixels of the first depth map of the third image. In various implementations, the depth values of the second depth map of the second image are averaged with (or otherwise incorporated into) depth values of the first depth map of the third image. 
     In various implementations, the method  700  further includes generating, based on the first depth map of the third image and the respective plurality of third pixel values, a second depth map (e.g., a dense depth map) of the third image including, for each of the respective plurality of third pixels, a depth of the scene for the respective pixel. In various implementations, the second depth map for the third image is generated in a substantially similar manner as the second depth map for the second image is generated (in block  740 ). 
     The device can use the second depth map of the third image in variety of ways to process the third image. For example, in various implementations, the method  700  further includes applying a depth-of-field effect to the third image based on the second depth map of the third image. As another example, in various implementations, the method  700  further includes detecting an object in the third image based on the second depth map of the third image. 
     In various implementations, the method  700  loops, from block  760 , to block  740 , where second depth maps for an image are generated based on first depth maps for the image and used to generate first depth maps for subsequently captured images. 
     Thus, in various implementations, the method  700  feeds back second depth maps to generate subsequent first depth maps for subsequent images (e.g., a fourth image, a fifth image, a sixth image, etc.) This feedback increases the accuracy of both the first depth maps and the second depth maps for subsequent images. The method  700  thus provides accurate depth maps that can be used to apply a depth-of-field effect, a lighting effect, or enhanced object detection. Further, as noted above, the method  700  can advantageously be performed by an electronic device with two scene cameras or only one scene camera. 
     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: 20190924
Publication Date: 20201110
Grant Date: 20201110
Priority Date: 20180927
Inventors: Ulbricht, Daniel
K C, AMIT KUMAR
BLECHSCHMIDT, ANGELA
LEE, CHEN-YU
VERMA, ESHAN
BAIG, MOHAMMAD HARIS
BATRA, TANMAY
Assignee: APPLE INC
CPC Classifications: [{"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/239", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N2013/0081", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/55", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/20084", "inventive": false, "first": false, "tree": "[]"}, {"code": "G02B2027/0138", "inventive": false, "first": false, "tree": "[]"}, {"code": "G02B2027/0187", "inventive": false, "first": false, "tree": "[]"}, {"code": "G02B27/017", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/11", "inventive": true, "first": false, "tree": "[]"}, {"code": "G02B27/01", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/30", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T7/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "G02B27/01", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/30", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T7/11", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01B11/24", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 73052003