PATENT DOCUMENT

Publication Number: US-11204783-B2
Application Number: US-201916548065-A
Country: US
Kind Code: B2

Title: Method and device for process data sharing

Abstract:
In one implementation, a method of accessing shared data among processes is performed by a device including processor(s), non-transitory memory, and an image acquisition interface. The method includes obtaining image data acquired by the image acquisition interface. The method further includes determining pose data based at least in part on inertial measurement unit (IMU) information measured by the image acquisition interface. The method also includes determining a gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface. Based at least in part on characteristics of processes, the method includes determining an arrangement for the image data, the pose data, and the gaze estimation. The method additionally includes determining an access schedule for the processes based at least in part on at least one of: the arrangement, the characteristics of the processes, and hardware timing parameters associated with the device.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a device with one or more processors, a non-transitory memory, and an image acquisition interface:
 obtaining image data associated with a field of view acquired by the image acquisition interface; 
 determining pose data based at least in part on inertial measurement unit (IMU) information, wherein the pose data corresponds to a current posture of a user measured by the image acquisition interface; 
 determining a gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface; 
 determining an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface; and 
 determining an access schedule for the plurality of processes based at least in part on at least one of: 
 the arrangement for the image data, the pose data, and the gaze estimation, 
 the plurality of characteristics of the plurality of processes, and 
 hardware timing parameters associated with the device; 
 wherein determining the arrangement for the image data, the pose data, and the gaze estimation based at least in part on the plurality of characteristics of the plurality of processes includes:
 obtaining at least one of a first image data, a first pose data, and a first gaze estimation for a first process of the plurality of processes, wherein the first process is characterized by a first set of characteristics; 
 obtaining at least one of a second image data, a second pose data, and a second gaze estimation for a second process of the plurality of processes, wherein the second process is characterized by a second set of characteristics; and 
 aggregating the first image data, the first pose data, and the first gaze estimation with the second image data, the second pose data, and the second gaze estimation to generate the image data, the pose data, and the gaze estimation, wherein the image data, the pose data, and the gaze estimation are arranged based at least in part on the first set of characteristics and the second set of characteristics. 
 
 
 
     
     
       2. The method of  claim 1 , wherein the access schedule is determined based on a deterministic model, and the method includes:
 determining, for a first process of the plurality of processes, inputs for the first process, at least one of a hardware or a second process providing the inputs, and a time parameter for obtaining the inputs from at least one of the hardware or the second process; and 
 calculating a waking time for the first process based at least in part on the time parameter. 
 
     
     
       3. The method of  claim 1 , further comprising:
 obtaining, from a base device, frames for rendering, wherein the frames are associated with timestamps; 
 predicting a rendering pose at a rendering time based on a trajectory of the pose data and the timestamps; and 
 adjusting the frames using the rendering pose at the rendering time. 
 
     
     
       4. The method of  claim 1 , wherein determining the access schedule for the plurality of processes includes:
 triggering collection of data by a base device, wherein the data includes at least one of the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, or the hardware timing parameters associated with the device; and 
 receiving the access schedule from the base device, wherein the access schedule is determined by the base device based on the data. 
 
     
     
       5. The method of  claim 1 , further comprising:
 accessing the arrangement according to the access schedule in order to generate a scene for display; and 
 displaying the scene using data obtained from the arrangement and according to the access schedule, wherein the data is a subset of at least one of the image data, the pose data, and the gaze estimation. 
 
     
     
       6. The method of  claim 5 , wherein accessing the arrangement according to the access schedule includes:
 determining an expected execution time for a process of the plurality of processes based on the access schedule; and 
 waking up the process for data access at the expected execution time upon fetching data used by the process according to the arrangement. 
 
     
     
       7. The method of  claim 1 , wherein the image data, the pose data, and the gaze estimation are stored in a storage device and the arrangement for the image data, the pose data, and the gaze estimation includes at least one of a layout or a sequencing for storing the image data, the pose data, and the gaze estimation in the storage device. 
     
     
       8. The method of  claim 1 , wherein the plurality of characteristics of the plurality of processes includes a sequence of the plurality of processes using the image data, the pose data, and the gaze estimation. 
     
     
       9. The method of  claim 8 , wherein the hardware timing parameters are associated with at least one of an image sensor, an IMU, an eye tracker, a controller, a processor, or a communication device. 
     
     
       10. A device comprising:
 an image acquisition interface; and 
 one or more processors to:
 obtain image data associated with a field of view acquired by the image acquisition interface; 
 determine pose data based at least in part on inertial measurement unit (IMU) information, wherein the pose data corresponds to a current posture of a user measured by the image acquisition interface; 
 determine gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface; 
 determine an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface; and 
 determine an access schedule for the plurality of processes based at least in part on at least one of: 
 the arrangement for the image data, the pose data, and the gaze estimation, 
 the plurality of characteristics of the plurality of processes, and 
 hardware timing parameters associated with the device; 
 wherein the one or more processors are to determine the arrangement for the image data, the pose data, and the gaze estimation based at least in part on the plurality of characteristics of the plurality of processes by:
 obtaining at least one of a first image data, a first pose data, and a first gaze estimation for a first process of the plurality of processes, wherein the first process is characterized by a first set of characteristics; 
 obtaining at least one of a second image data, a second pose data, and a second gaze estimation for a second process of the plurality of processes, wherein the second process is characterized by a second set of characteristics; and 
 aggregating the first image data, the first pose data, and the first gaze estimation with the second image data, the second pose data, and the second gaze estimation to generate the image data, the pose data, and the gaze estimation, wherein the image data, the pose data, and the gaze estimation are arranged based at least in part on the first set of characteristics and the second set of characteristics. 
 
 
 
     
     
       11. The device of  claim 10 , wherein the access schedule is determined based on a deterministic model, and one or more processors are further to:
 determine, for a first process of the plurality of processes, inputs for the first process, at least one of a hardware or a second process providing the inputs, and a time parameter for obtaining the inputs from at least one of the hardware or the second process; and 
 calculate a waking time for the first process based at least in part on the time parameter. 
 
     
     
       12. The device of  claim 10 , wherein the one or more processors are further to:
 access the arrangement according to the access schedule in order to generate a scene for display; and 
 display the scene using data obtained from the arrangement and according to the access schedule, wherein the data is a subset of at least one of the image data, the pose data, and the gaze estimation. 
 
     
     
       13. The device of  claim 10 , wherein the plurality of characteristics of the plurality of processes includes a sequence of the plurality of processes using the image data, the pose data, and the gaze estimation. 
     
     
       14. A method comprising:
 at a device with one or more processors, a non-transitory memory, and an image acquisition interface:
 obtaining image data associated with a field of view acquired by the image acquisition interface; 
 determining pose data based at least in part on inertial measurement unit (IMU) information, wherein the pose data corresponds to a current posture of a user measured by the image acquisition interface; 
 determining a gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface; 
 determining an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface; and 
 determining an access schedule for the plurality of processes based at least in part on at least one of: 
 the arrangement for the image data, the pose data, and the gaze estimation, 
 the plurality of characteristics of the plurality of processes, and 
 hardware timing parameters associated with the device; 
 wherein the access schedule is determined based on a deterministic model, and the method includes:
 determining, for a first process of the plurality of processes, inputs for the first process, at least one of a hardware or a second process providing the inputs, and a time parameter for obtaining the inputs from at least one of the hardware or the second process; and 
 calculating a waking time for the first process based at least in part on the time parameter. 
 
 
 
     
     
       15. The method of  claim 14 , wherein determining the access schedule for the plurality of processes includes:
 triggering collection of data by a base device, wherein the data includes at least one of the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, or the hardware timing parameters associated with the device; and 
 receiving the access schedule from the base device, wherein the access schedule is determined by the base device based on the data. 
 
     
     
       16. The method of  claim 14 , further comprising:
 accessing the arrangement according to the access schedule in order to generate a scene for display; and 
 displaying the scene using data obtained from the arrangement and according to the access schedule, wherein the data is a subset of at least one of the image data, the pose data, and the gaze estimation. 
 
     
     
       17. The method of  claim 16 , wherein accessing the arrangement according to the access schedule includes:
 determining an expected execution time for a process of the plurality of processes based on the access schedule; and 
 waking up the process for data access at the expected execution time upon fetching data used by the process according to the arrangement. 
 
     
     
       18. A method comprising:
 at a device with one or more processors, a non-transitory memory, and an image acquisition interface:
 obtaining image data associated with a field of view acquired by the image acquisition interface; 
 determining pose data based at least in part on inertial measurement unit (IMU) information, wherein the pose data corresponds to a current posture of a user measured by the image acquisition interface; 
 determining a gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface; 
 determining an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface; and 
 determining an access schedule for the plurality of processes based at least in part on at least one of: 
 the arrangement for the image data, the pose data, and the gaze estimation, 
 the plurality of characteristics of the plurality of processes, and 
 hardware timing parameters associated with the device; 
 wherein determining the access schedule for the plurality of processes includes:
 triggering collection of data by a base device, wherein the data includes at least one of the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, or the hardware timing parameters from the device; and 
 receiving the access schedule from the base device, wherein the access schedule is determined by the base device based on the data. 
 
 
 
     
     
       19. The method of  claim 18 , wherein the data are also collected by the base device from other devices. 
     
     
       20. The method of  claim 18 , further comprising:
 obtaining, from the base device, frames for rendering, wherein the frames are associated with timestamps; 
 predicting a rendering pose at a rendering time based on a trajectory of the pose data and the timestamps; and 
 adjusting the frames using the rendering pose at the rendering time. 
 
     
     
       21. The method of  claim 18 , further comprising:
 accessing the arrangement according to the access schedule in order to generate a scene for display; and 
 displaying the scene using data obtained from the arrangement and according to the access schedule, wherein the data is a subset of at least one of the image data, the pose data, and the gaze estimation, 
 wherein accessing the arrangement according to the access schedule includes:
 determining an expected execution time for a process of the plurality of processes based on the access schedule; and 
 waking up the process for data access at the expected execution time upon fetching data used by the process according to the arrangement. 
 
 
     
     
       22. A device comprising:
 an image acquisition interface; and 
 one or more processors to:
 obtain image data associated with a field of view acquired by the image acquisition interface; 
 determine pose data based at least in part on inertial measurement unit (IMU) information, wherein the pose data corresponds to a current posture of a user measured by the image acquisition interface; 
 determine gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface; 
 determine an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface; and 
 determine an access schedule for the plurality of processes based at least in part on at least one of: 
 the arrangement for the image data, the pose data, and the gaze estimation, 
 the plurality of characteristics of the plurality of processes, and 
 hardware timing parameters associated with the device 
 wherein the access schedule is determined based on a deterministic model, and one or more processors are further to:
 determine, for a first process of the plurality of processes, inputs for the first process, at least one of a hardware or a second process providing the inputs, and a time parameter for obtaining the inputs from at least one of the hardware or the second process; and 
 calculate a waking time for the first process based at least in part on the time parameter. 
 
 
 
     
     
       23. The device of  claim 22 , wherein the one or more processors are to determine the access schedule for the plurality of processes by:
 triggering collection of data by a base device, wherein the data includes at least one of the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, or the hardware timing parameters associated with the device; and 
 receiving the access schedule from the base device, wherein the access schedule is determined by the base device based on the data. 
 
     
     
       24. The device of  claim 22 , wherein the one or more processors are further to:
 access the arrangement according to the access schedule in order to generate a scene for display; and 
 display the scene using data obtained from the arrangement and according to the access schedule, wherein the data is a subset of at least one of the image data, the pose data, and the gaze estimation. 
 
     
     
       25. The device of  claim 24 , wherein the one or more processors are to access the arrangement according to the access schedule by:
 determining an expected execution time for a process of the plurality of processes based on the access schedule; and 
 waking up the process for data access at the expected execution time upon fetching data used by the process according to the arrangement. 
 
     
     
       26. A device comprising:
 an image acquisition interface; and 
 one or more processors to:
 obtain image data associated with a field of view acquired by the image acquisition interface; 
 determine pose data based at least in part on inertial measurement unit (IMU) information, wherein the pose data corresponds to a current posture of a user measured by the image acquisition interface; 
 determine gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface; 
 determine an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface; and 
 determine an access schedule for the plurality of processes based at least in part on at least one of: 
 the arrangement for the image data, the pose data, and the gaze estimation, 
 the plurality of characteristics of the plurality of processes, and 
 hardware timing parameters associated with the device; 
 wherein the one or more processors are to determine the access schedule for the plurality of processes by:
 triggering collection of data by a base device, wherein the data includes at least one of the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, or the hardware timing parameters from the device; and 
 receiving the access schedule from the base device, wherein the access schedule is determined by the base device based on the data. 
 
 
 
     
     
       27. The device of  claim 26 , wherein the data are also collected by the base device from other devices. 
     
     
       28. The device of  claim 26 , wherein the one or more processors are further to:
 obtain, from the base device, frames for rendering, wherein the frames are associated with timestamps; 
 predict a rendering pose at a rendering time based on a trajectory of the pose data and the timestamps; and 
 adjust the frames using the rendering pose at the rendering time. 
 
     
     
       29. The device of  claim 26 , wherein the one or more processors are further to:
 access the arrangement according to the access schedule in order to generate a scene for display; and 
 display the scene using data obtained from the arrangement and according to the access schedule, wherein the data is a subset of at least one of the image data, the pose data, and the gaze estimation. 
 
     
     
       30. The device of  claim 29 , wherein the one or more processors are to accessing the arrangement according to the access schedule by:
 determining an expected execution time for a process of the plurality of processes based on the access schedule; and 
 waking up the process for data access at the expected execution time upon fetching data used by the process according to the arrangement.

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to data sharing, and in particular, to systems, methods, and devices providing low-latency data sharing using a deterministic pull/fetch model. 
     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 smartphones, tablets, desktop/laptop computers, 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 and/or cameras having hand tracking and/or other body pose estimation abilities). 
     A head-mounted system may have one or more speaker(s) and an integrated opaque display. Alternatively, a head-mounted system may be a head-mounted enclosure (HME) 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 implementation, 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. 
     Content available on CGR devices is becoming more immersive, more graphically intensive, and universally applicable to everyday lives. Thus, the hardware in CGR devices continues to evolve to accommodate resource-heavy processes in order to keep up with the CGR content. However, with multiple processes contending for resources at once, latency and a large number of interrupts naturally create a bottleneck effect. Visible latency issues can adversely affect a user&#39;s experience. 
    
    
     
       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 exemplary operating environment in accordance with some implementations. 
         FIG. 2  is a block diagram illustrating an interrupt model for CGR data sharing in accordance with some implementations. 
         FIG. 3  is a block diagram of an exemplary deterministic pull/fetch model for CGR data sharing among processes in accordance with some implementations. 
         FIG. 4  is a block diagram illustrating an exemplary CGR data sharing process based on a deterministic pull/fetch model in accordance with some implementations. 
         FIG. 5A  is a block diagram of an example of a controller that is used in a pull/fetch model in accordance with some implementations. 
         FIG. 5B  is a block diagram illustrating an exemplary controller managed data arrangement and process access scheduling in accordance with some implementations. 
         FIG. 6  is a block diagram of an exemplary CGR device in accordance with some implementations. 
         FIG. 7A  illustrates a CGR pipeline in accordance with some implementations. 
         FIG. 7B  illustrates a distributed system implementing the CGR pipeline in accordance with some implementations. 
         FIG. 8  is a flowchart representation of a method of accessing shared data among processes 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 accessing shared data among processes. In various implementations, the method is performed at a device including one or more processors, non-transitory memory, and an image acquisition interface. The method includes obtaining image data associated with a field of view acquired by the image acquisition interface. The method further includes determining pose data based at least in part on inertial measurement unit (IMU) information, where the pose data corresponds to a current posture of the user measured by the image acquisition interface. The method additionally includes determining a gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface. The method further also includes determining an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface. The method further includes determining an access schedule for the plurality of processes based at least in part on at least one of: the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, and hardware timing parameters associated with the device. 
     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. 
     As described above, processes on CGR devices following an interrupt model for resources often content for resources simultaneously. As a result, the bottleneck effect may cause latency that adversely affect the user&#39;s experience. Various implementations disclosed herein move away from the interrupt model towards a deterministic pull/fetch model. An access schedule is determined that allows the processes to fetch data at set times. The access schedule is determined based on known (e.g., deterministic) information, such as system parameters and user pose information. Accordingly, various implementations described herein address the above mentioned shortfalls, specifically those involved in data access. As a result, fewer memory and processing resources are consumed. It naturally follows that because latencies are decreased when data are through the CGR display pipeline, the overall user experience can be improved. 
       FIG. 1  is a block diagram of an exemplary 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  102  and a CGR device  104 . 
     In some implementations, the CGR device  104  corresponds to tablet or mobile phone. In various implementations, the CGR device  104  corresponds to a head-mounted system, such as a head-mounted device (HMD) or a head-mounted enclosure (HME) having a tablet or mobile phone inserted therein. In some implementations, the CGR device  104  is configured to present CGR content to a user. In some implementations, the CGR device  104  includes a suitable combination of software, firmware, and/or hardware. 
     According to some implementations, the CGR device  104  presents, via a display  122 , CGR content to the user while the user is virtually and/or physically present within a scene  106  that includes a table  107  within the field-of-view  111  of the CGR device  104 . In some implementations, the CGR device  104  is configured to present virtual content (e.g., the virtual cylinder  109 ) and to enable video pass-through of the scene  106  (e.g., including a representation  117  of the table  107 ) on a display  122 . In some implementations, the CGR device  104  is configured to present virtual content and to enable optical see-through of the scene  106 . 
     In some implementations, the user holds the CGR device  104  in his/her hand(s). In some implementations, the user wears the CGR device  104  on his/her head. As such, the CGR device  104  includes one or more CGR displays provided to display the CGR content. For example, the CGR device  104  encloses the field-of-view of the user. In some implementations, the CGR device  104  is replaced with a CGR chamber, enclosure, or room configured to present CGR content in which the user does not wear the CGR device  104 . 
     In some implementations, the controller  102  is configured to manage and coordinate presentation of CGR content for the user. In some implementations, the controller  102  includes a suitable combination of software, firmware, and/or hardware. In some implementations, the controller  102  is a computing device that is local or remote relative to the scene  106 . For example, the controller  102  is a local server located within the scene  106 . In another example, the controller  102  is a remote server located outside of the scene  106  (e.g., a cloud server, central server, etc.). In some implementations, the controller  102  is communicatively coupled with the CGR device  104  via one or more wired or wireless communication channels  144  (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x, etc.). In some implementations, the functionalities of the controller  102  are provided by and/or combined with the CGR device  104 . 
     As illustrated in  FIG. 1 , the CGR device  104  presents a representation of the scene  106 . In some implementations, the representation of the scene  106  is generated by the controller  102  and/or the CGR device  104 . In some implementations, the representation of the scene  106  includes a virtual scene that is a simulated replacement of the scene  106 . In other words, in some implementations, the representation of the scene  106  is simulated by the controller  102  and/or the CGR device  104 . In such implementations, the representation of the scene  106  is different from the scene  106  where the CGR device  104  is located. In some implementations, the representation of the scene  106  includes an augmented scene that is a modified version of the scene  106  (e.g., including the virtual cylinder  109 ). For example, in some implementations, the controller  102  and/or the CGR device  104  modify (e.g., augment) an image of the scene  106  in order to generate the representation of the scene  106 . In some implementations, the controller  102  and/or the CGR device  104  generate the representation of the scene  106  by simulating a replica of the scene  106 . In some implementations, the controller  102  and/or the CGR device  104  generate the representation of the scene  106  by removing and/or adding items from the simulated replica of the scene  106 . 
       FIG. 2  is a block diagram illustrating an interrupt model  200  for CGR data sharing. In some implementations, in order to generate the representation of the scene  106  (as illustrated in  FIG. 1 ), a plurality of processes, e.g., process  1   210 - 1 , process  2   210 - 2 , process  3   210 - 3  . . . process N  210 -N, obtain raw data acquired by the CGR device  104  (as illustrated in  FIG. 1 ) as inputs. For example, the raw data includes image data  230  acquired by image sensor(s), pose data  240  acquired by an IMU, gaze estimation data  250  derived from information obtained by an eye tracker, and other data  260  acquired by the CGR device  104 . In some implementations, the plurality of processes  210  are dependent upon each other, such that outputs from one process are used by another process as inputs, e.g., outputs from process  2   210 - 2  are inputs to process  3   210 - 3 . In some implementation, the raw data and/or the outputs from processes  210  are communicated through a communication path  220 , e.g., a communication path established through communication interface(s) of the controller  102  and/or the communication interface(s) of the CGR device  104 . 
     As shown in  FIG. 2 , when process  1   210 - 1  needs the image data  230  and the gaze estimation data  250 , process  1   210 - 1  interrupts the image sensor and the eye tracker in order to obtain a copy of the image data  230  and a copy of the gaze estimation data  250  as inputs. Likewise, when process  2   210 - 2  also needs the image data  230  and the gaze estimation data  250 , process  2   210 - 2  interrupts the image sensor and the eye tracker in order to obtain a copy of the image data  230  and a copy of the pose estimation data  250  as inputs. In another example, as shown in  FIG. 2 , process  3   210 - 3  needs inputs from process  2   210 - 2 , the pose data  240 , and the other data  260 . Process  3   210 - 3  would wait for the completion of process  2   210 - 2  while interrupting the IMU and other sensor(s) in order to obtain the pose data  240  and the other data  260 . 
     The interrupt model  200  is inefficient for several reasons. First, when multiple processes (e.g., process  1   210 - 1  and process  2   210 - 2 ) are contending for resources (e.g., the image data  230  and the gaze estimation data  250 ), the interrupts created multiple bottlenecks, e.g., at least one bottleneck at the sensors and another at the communication path  220 . Second, because multiple copies of the data are created for multiple processes, the interrupt model  200  does not share memory across different tasks. As such, the memory usage is inefficient. Due to the bottlenecks, the inefficient memory utilization, and the cascade effect from process dependencies, the interrupt model  200  cannot meet the latency requirement for real-time streaming of CGR content. As such, the CGR scene presented using the interrupt model  200  can cause motion sickness for a user. 
       FIG. 3  is a block diagram of an example deterministic pull/fetch model  300  for CGR data sharing among processes in accordance with some embodiments. A deterministic system typically involves no randomness in the development of future states of the system. A deterministic model will thus produce the same output from a given starting condition or an initial state. As such, using the deterministic model  300 , a system can predict when data from where would be produced and accessed. As such, the pull/fetch model  300  allows processes to retrieve data when the data are ready to be fetched and from a location efficient for the retrieval. Accordingly, in such system, bottlenecks are reduced and contention for resources goes down. 
     For example,  FIG. 3  shows two processes, namely process M and process N, where process N depends on outputs from process M and process M fetches image data taken by a camera. The system learns hardware timing parameters, such as camera exposure time  41 , as well as characteristics of the processes, e.g., processing time  42  for process M and/or the type of input data for processes M and N. These parameters and characteristics are used by the system to determine when a process would fetch data and from which location the data would be fetched. In  FIG. 3 , the system uses a system synchronization clock to measure the starting time (e.g., T 1 ), the expected camera operation duration (e.g., from T 2  to T 3 ), and the expected process M execution time (from T 4  to T 5 ) and calculates the wakeup time T 4  for process M and wakeup time T 6  for process N. 
     In some embodiments, the deterministic system generates an access schedule for the processes including the calculated wakeup time for each process. In some embodiments, between each task, a threshold amount of time is reserved, e.g., the periods between T 1  and T 2 , between T 3  and T 4 , and between T 5  and T 6 . The threshold amount of time is reserved for communication latency between processes and/or hardware components, e.g., the time between T 1  and T 2  is for the system notifying the camera to start the image data acquisition, the time between T 3  and T 4  is for the camera waking up process M, and the time between T 5  and T 6  is for process M waking up process N. In some embodiments, the threshold amount of time is also recorded in the access schedule. Following the access schedule, upon waking up, the inputs for processes M and N are ready to be fetched. Thus, in contrast to the interrupt model  200  as shown in  FIG. 2 , the pull/fetch model  300  as shown in  FIG. 3  reduces constraints at a system level and increases system performance as a whole. 
       FIG. 4  is a block diagram illustrating a CGR data sharing process  400  based on the deterministic pull/fetch model  300  in accordance with some implementations. In some implementations, in order to generate the representation of the scene  106  (as illustrated in  FIG. 1 ), a plurality of processes, e.g., process  1   410 - 1 , process  2   410 - 2 , process  3   410 - 3  . . . process N  410 -N, receive raw data acquired by the CGR device  104  (as illustrated in  FIG. 1 ). For example, the raw data include image data  430  acquired by image sensor(s), pose data  440  acquired by an IMU, gaze estimation data  450  derived from information obtained by an eye tracker, and other data  460  acquired by the CGR device  104 . In some implementations, the plurality of processes are dependent upon each other, such that outputs from one process are used by another process as inputs, e.g., outputs from process  2   410 - 2  are inputs to process  3   410 - 3 . In some implementation, the raw data and/or the outputs from the plurality of processes  410  are communicated through a communication path  420 , e.g., a communication path established through communication interface(s) of the controller  102  and/or the communication interface(s) of the CGR device  104 . 
     As shown in  FIG. 4 , using the deterministic model  300 , the controller  102  directs data arrangements (e.g., layout and/or sequencing) for data  427  in a buffer  425  and wakes up the plurality of processes  410  according to an access schedule, e.g., when the data in the buffer  425  are ready to be pulled/fetched. For example, the image data  427 - 2  for process  2   410 - 2  (represented as (I,  2 ) in  FIG. 4 ) and the image data  427 - 4  for process  3   410 - 3  (represented as (I,  3 ) in  FIG. 4 ) are obtained from the camera captured image data  430 . The image data  427 - 2  for process  2   410 - 2  and the image data  427 - 4  for process  3   410 - 3  are arranged in the buffer  425  in a layout and/or sequence such that when the image data  427 - 2  and  427 - 4  are provided to the processes  410  in a data stream, and process  2   410 - 2  and process  3   410 - 3  wake up in sequence according to the access schedule. In particular, when process  3   410 - 3  wakes up, the image data  427 - 4  as well as the outputs from process  2   410 - 2  are ready to be pulled/fetched. 
       FIG. 5A  is a block diagram of an example of the controller  102  that is used in the pull/fetch model described above in accordance with some implementations.  FIG. 5B  is a block diagram illustrating the data arrangement and process access scheduling managed by the controller  102  in accordance with some implementations.  FIG. 5B  is used to illustrate an exemplary CGR scene generation managed by the controller  102  shown in  FIG. 5A  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, as shown in  FIG. 5A , in some implementations the controller  102  includes one or more processing units  502  (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  506 , one or more communication interfaces  508  (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  510 , a memory  520 , and one or more communication buses  504  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  504  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices  506  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  520  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  520  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  520  optionally includes one or more storage devices remotely located from the one or more processing units  502 . The memory  520  comprises a non-transitory computer readable storage medium. In some implementations, the memory  520  or the non-transitory computer readable storage medium of the memory  520  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  530  and a CGR content module  540 . 
     The operating system  530  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR content module  540  is configured to manage and coordinate presentation of CGR content for one or more users (e.g., a single set of CGR content for one or more users, or multiple sets of CGR content for respective groups of one or more users). To that end, in various implementations, the CGR content module  540  includes a data obtaining unit  542 , a tracking unit  544 , a coordination unit  546 , a data transmitting unit  548 , and a data access unit  550 . 
     In some implementations, the data obtaining unit  542  is configured to obtain data (e.g., image data, pose data, gaze estimation, presentation data, interaction data, sensor data, location data, etc.) from at least the CGR device  104 . To that end, in various implementations, the data obtaining unit  542  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the tracking unit  544  is configured to map the scene  106  and to track the position/location of at least the CGR device  104  with respect to the scene  106 . To that end, in various implementations, the tracking unit  544  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the coordination unit  546  is configured to manage and coordinate the presentation of CGR content to the user by the CGR device  104 . In order to manage and coordinate the presentation of CGR content, in some implementations, the coordination unit  546  is configured to obtain information related to processes, data storage, and hardware characteristics. The information is then used by the coordination unit  546  to coordinate the processes at the system level. To that end, in various implementations, the coordination unit  546  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data transmitting unit  548  is configured to transmit data (e.g., presentation data, location data, etc.) to at least the CGR device  104 . To that end, in various implementations, the data transmitting unit  548  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the data access unit  550  is configured to determine an access schedule that allows processes to fetch data obtained from the data obtaining unit  542 . In some implementations, the data access unit  550  determines a data arrangement  552  for efficient data retrieval by the processes. In some implementations, the data access unit  550  also determines an access schedule  554  for the processes to pull or fetch the data. To that end, in various implementations, the data transmitting unit  548  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     For example, in  FIG. 5B , a scene recognition process  560  obtains image data and pose data  580  for recognizing the scene  106 . As an eye tracker detects a user fixing gaze proximate to a region of interest (ROI)  572  within the scene  106 , a ROI recognition process  562  identifies ROI image data  590  in the image data based on gaze estimation data  582 . Subsequently, an object recognition process  564  analyzes the ROI image data  590  and recognizes an object  574  within the ROI  572 . As shown in  FIG. 5B , the processes  560 ,  562 , and  564  access the data arrangement  552  according to the access schedule  554 , where the access schedule  554  includes information such as a starting time for each process. Also as shown in  FIG. 5B , data in the data arrangement  552  are arranged to accommodate the scheduled fetching. For instance, since the ROI image data  590  are used by the ROI recognition process  562  and the object recognition process  564 , the ROI image data  590  is stored separately from non-ROI image data  592  to accommodate more frequent or more urgent access. 
     Referring back to  FIG. 5A , although the data obtaining unit  542 , the tracking unit  544 , the coordination unit  546 , the data transmitting unit  548 , and the data access unit  550  are shown as residing on a single device (e.g., the controller  102 ), it should be understood that in other implementations, any combination of the data obtaining unit  542 , the tracking unit  544 , the coordination unit  546 , the data transmitting unit  548 , and the data access scheduling unit  550  may be located in separate computing devices. 
     Moreover,  FIG. 5A  is intended more as functional description of the various features which are present in a particular embodiment 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. 5A  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 embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
       FIG. 6  is a block diagram of an exemplary CGR device  104  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 CGR device  104  includes one or more processing units  602  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices  606 , one or more communication interfaces  608  (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  610 , one or more CGR displays  612 , one or more image acquisition interfaces  614  (e.g., optional interior and/or exterior facing image sensors), a memory  620 , and one or more communication buses  604  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  604  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices  606  include at least one of 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, and/or the like. 
     In some implementations, the one or more CGR displays  612  are configured to present CGR content to the user. In some embodiments, the one or more CGR displays  612  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 embodiments, the one or more CGR displays  612  correspond to diffractive, reflective, polarized, holographic, etc. waveguide displays. For example, the CGR device  104  includes a single AR/VR display. In another example, the CGR device  104  includes an CGR display for each eye of the user. 
     In some implementations, the one or more image acquisition interfaces  614  are configured to obtain data for CGR content generation. In some implementations, the one or more image acquisition interfaces  614  include at least one of one or more image sensors, an inertial measurement unit (IMU), an accelerometer, a gyroscope, a thermometer, one or more depth sensors (e.g., a structured light, a time-of-flight, or the like), an eye tracker. For example, the one or more image sensors correspond to one or more RGB camera (e.g., with a complimentary metal-oxide-semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor), IR camera, event-based camera, and/or the like. 
     The memory  620  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  620  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  620  optionally includes one or more storage devices remotely located from the one or more processing units  602 . The memory  620  comprises a non-transitory computer readable storage medium. In some implementations, the memory  620  or the non-transitory computer readable storage medium of the memory  620  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  630  and a CGR presentation module  640 . 
     The operating system  630  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the CGR presentation module  640  is configured to present CGR content to the user via the one or more CGR displays  612 . To that end, in various implementations, the CGR presentation module  640  includes a data obtaining unit  642 , a CGR presenting unit  644 , an eye tracking unit  646 , and a data transmitting unit  648 . 
     In some implementations, the data obtaining unit  642  is configured to obtain data (e.g., image data, pose data, presentation data, interaction data, sensor data, location data, etc.) from at least the controller  102 . To that end, in various implementations, the data obtaining unit  642  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the CGR presenting unit  644  is configured to present CGR content via the one or more CGR displays  612 . To that end, in various implementations, the CGR presenting unit  644  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the CGR presenting unit  644  is configured to project an image comprising emitted light in a first wavelength range through an eyepiece that distorts light in the first wavelength range. In some embodiments, the CGR presenting unit  644  is configured to project an image comprising emitted light in a first wavelength through an eyepiece that reflects and refracts light in the first wavelength range while passing, without substantial distortion, light in the second wavelength range. 
     In some implementations, the eye tracking unit  646  is configured to emit, using one or more light sources disposed between the eyepiece and the display, light in a second wavelength range and detect, using a camera, the light in the second wavelength range. In various implementations, the one or more light sources illuminate the eye of a user and the camera detect light reflected from the eye of the user. To that end, in various implementations, the eye tracking unit  646  includes instructions and/or logic therefor, and heuristics and metadata therefor. In some implementations, the eye tracking unit  646  is configured to emitting light in a second wavelength range through the eyepiece and detecting the light in the second wavelength range reflected by the eye of a user. In some implementations, the eye tracking unit  646  provides a gaze estimation based at least in part on the detected light reflection from the eye of the user. 
     In some implementations, the data transmitting unit  648  is configured to transmit data (e.g., presentation data, location data, etc.) to at least the controller  102 . To that end, in various implementations, the data transmitting unit  648  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the data obtaining unit  642 , the CGR presenting unit  644 , the eye tracking unit  646 , and the data transmitting unit  648  are shown as residing on a single device (e.g., the CGR device  104 ), it should be understood that in other implementations, any combination of the data obtaining unit  642 , the CGR presenting unit  644 , the eye tracking unit  646 , and the data transmitting unit  648  may be located in separate computing devices. 
     Moreover,  FIG. 6  is intended more as functional description of the various features which are present in a particular embodiment 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. In some implementations, some functional modules shown separately in  FIG. 6  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 embodiment to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular embodiment. 
     Additionally, in some implementations, the functions performed by the controller  102  as shown in  FIG. 5A  and the CGR device  104  as shown in  FIG. 6  are distributed among devices. For example,  FIG. 7A  illustrates a CGR pipeline  700  that receives data from sensors and generates the representation of the scene  106  (as illustrated in  FIG. 1 ) in accordance with some implementations.  FIG. 7B  illustrates a system  750  that implements the CGR pipeline  700 . The system  750  distributes tasks performed by the controller  102  and/or the CGR device  104  described above between the controller  102  and the CGR device  104 . 
     As shown in  FIG. 7A , in some implementations, the image acquisition interface  614  ( FIG. 6 ) includes at least an image sensor  710  for outputting image data, an IMU  712  for outputting pose data, an eye tracker  714  for providing gaze estimation, and one or more other sensors  716  for providing raw data as inputs to processes in order to generate the scene  106 . Upon receiving the image data, pose data, gaze estimation data, and raw data, processes executed by the controller  102  stores the received data in a buffer (e.g., the buffer  425  in  FIG. 4  or the buffer  720  in  FIG. 7A ) according to the data arrangement  552  as explained above. Further, the processes executed by the controller  102  accesses the buffer  720  according to the access schedule  554 . The access schedule  554  is generated according to the deterministic model as described above with reference to  FIG. 3 . 
     In some implementations, the CGR pipeline  700  includes a rendering module  730  that receives CGR content and the data from the buffer  720  and renders an image on the display  612 . In various implementations, the CGR content includes definitions of geometric shapes of virtual objects, colors and/or textures of virtual objects, images (such as a see-through image of the scene  106 ), and other information describing content to be represented in the rendered image. In some implementations, final correction is performed prior to displaying the rendered image. For example, based on the pose data, the rendered image is corrected to improve the user&#39;s experience. 
     In some implementations, the final correction and other less computationally-intensive tasks (e.g., sensor data preprocessing) are performed at the CGR device, as shown in  FIG. 7B . The system  750  in  FIG. 7B , which implements the CGR pipeline  700  according to some embodiments, distributes the functions performed by the controller  102  and/or the CGR device  104 . As such, more computationally-intensive tasks are performed at the controller  102 , e.g., using one or more processors  756  of the controller  102  for tasks such as machine learning, computer vision, and/or 3D rendering etc. The controller  102  then transports a computed image to the CGR device  104  for final correction before display. In some implementations, the transportation process includes compression/decompression and communications between the controller  102  and the CGR device  104 . 
     In some implementations, the one or more processors  756  includes the coordination unit  546  and the data access unit  550 , which further includes the data arrangement  552 - 1  and the access schedule  554 - 1  for processes distributed across the system  750 . For example, the controller  102  collects characteristics of processes and hardware parameters from the CGR device  104 . Using machine learning such as neural networks, characteristics of processes across platforms, including the transportation process, the sensor data preprocessing, and/or the final correction etc., can be extracted and weights are assigned, so that a sequencing of the processes accessing data can be predicted. The controller  102  can also determines the data arrangement  552 - 1  to accommodate the predicted data access based at least in part on the access schedule  554 - 1  in some implementations. Though  FIG. 7B  illustrates the system  750  comprising one controller  102  and one CGR device  104 , in some embodiments, multiple CGR devices can connect to the controller  102 . In such embodiments, the machine learning on the controller  102  can be used to predict access schedule for cross-platform processes including processes across multiple CGR devices (e.g., in a multi-player CGR environment). In some implementations, the controller  102  distributes the system wide data arrangement  552 - 1  and access schedule  554 - 1 , so that each CGR device obtains a copy or a subset of the data arrangement  552 - 1  and/or the access schedule  554 - 1  from the controller  102 . According to a local copy of the data arrangement  552 - 2  and a local copy of the access schedule  554 - 2 , data acquired by the image acquisition interface  614  are arranged and processes associated with the CGR device  104  access the arranged data according to the schedule. As such, different from other systems, where each process or each device may have been optimized for data access, the system  750  according to embodiments described herein provides platform agnostic scheduling for data access at system level, so that latency is reduced as a whole. 
       FIG. 8  is a flowchart representation of a method  800  of determining an access schedule for processes in accordance with some implementations. In various implementations, the method  800  is performed by a device with one or more processors, non-transitory memory, and an image acquisition interface (e.g., the image acquisition interface  614  in  FIG. 6 ). In some implementations, the method  800  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  800  is performed by a processor and/or a controller (e.g., the controller  102  in  FIG. 1 ) executing instructions (e.g., code) stored in a non-transitory computer-readable medium (e.g., a memory). Briefly, in some circumstances, the method  800  includes: utilizing the image acquisition interface to obtain image data, determine pose data, and determine a gaze estimation; determining an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface; and determining an access schedule for the plurality of processes based at least in part on at least one of: the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, and hardware timing parameters associated with the device. 
     The method  800  begins, in block  810 , with the device obtaining image data associated with a field of view acquired by the image acquisition interface. In some embodiments, the image acquisition interface includes an image sensor (e.g., the image sensor  710  in  FIG. 7A ) for acquiring the image data associated with the field of view. 
     The method  800  continues, in block  820 , with the device determining pose data based at least in part on inertial measurement unit (IMU) information, where the pose data corresponds to a current posture of the user measured by the image acquisition interface. In some embodiments, the image acquisition interface includes an IMU (e.g., the IMU  712  in  FIG. 7A ) for measuring the current posture of the user. 
     The method  800  continues, in block  830 , with the device determining a gaze estimation based at least in part on eye tracking information obtained through the image acquisition interface. In some embodiments, the image acquisition interface includes an eye tracker (e.g., the eye tracker  714  in  FIG. 7A ) for obtaining the eye tracking information. 
     The method  800  continues, in block  840 , with the device determining an arrangement for the image data, the pose data, and the gaze estimation based at least in part on a plurality of characteristics of a plurality of processes communicable with the image acquisition interface. For example, as shown in  FIG. 4 , the arrangement of the data  427  in the buffer  425  is determined by the controller  102  using the deterministic model  300 . The deterministic model  300 , as shown in  FIG. 3 , obtains characteristics (e.g., execution sequencing and/or process execution duration) of processes as part of the parameters for determining the arrangement of the data  427  in the buffer  425 . 
     In some embodiments, as represented by block  842 , the arrangement determination includes aggregating data for different processes and arranging the aggregated data based at least in part on the plurality of characteristics of the plurality of processes. For instance, the arrangement determination for two processes includes the steps of: (1) obtaining at least one of a first image data, a first pose data, and a first gaze estimation for a first process of the plurality of processes, where the first process is characterized by a first set of characteristics; (2) obtaining at least one of a second image data, a second pose data, and a second gaze estimation for a second process of the plurality of processes, wherein the second process is characterized by a second set of characteristics; and (3) aggregating the first image data, the first pose data, and the first gaze estimation with the second image data, the second pose data, and the second gaze estimation to generate the image data, the pose data, and the gaze estimation, wherein the image data, the pose data, and the gaze estimation are arranged based at least in part on the first set of characteristics and the second set of characteristics. 
     For example, as shown in  FIG. 4 , the arrangement of data  427  in the buffer  425  is determined based at least in part on the characteristics of the processes  410 . In case the outputs from process  2   410 - 2  are used by process  3   410 - 3  as inputs, the data  427 - 2  for process  2   410 - 2  is arranged in the buffer such that it is ready to be fetched by process  2   410 - 2  before the data  427 - 4  is ready to be fetched by process  3   410 - 3 . In another example, as shown in  FIG. 5B , the ROI image data  590  is used by more processes than the non-ROI image data  592 . As such, according to the data arrangement  552 , the ROI image data  590  is stored at a more frequently access region and/or a region for more urgent needs. 
     Still referring to  FIG. 8 , the method  800  continues, in block  850 , with the device determining an access schedule for the plurality of processes based at least in part on at least one of: the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, and hardware timing parameters associated with the device. In some embodiments, as represented by block  852 , the method  800  includes determining the access schedule based on a deterministic model. In such embodiments, the method  800  further includes determining, for a first process of the plurality of processes, inputs for the first process, at least one of a hardware or a second process providing the inputs, and a time parameter for obtaining the inputs from at least one of the hardware or the second process; and calculating a waking time for the first process based at least in part on the time parameter. For example, as shown in  FIG. 3 , for process M, the waking time for execution of process M is determined at least in part on the exposure time of the camera, e.g., known Δ 1 . For process N, the waking time for execution of process M is determined at least in part on the exposure time of the camera and the execution duration of process M, e.g., known Δ 1  and known Δ 2 . 
     In some embodiments, as represented by block  854 , the method  800  includes distributing computation-intensive tasks to a base device and performing a final correction at the device in order to optimize the user experience (e.g., reduce motion sickness). For example, in the CGR pipeline  700  as shown in  FIG. 7A , the computation-intensive tasks including rendering (as performed by the rendering module  730 ) can be performed by the controller  102 , as shown in  FIG. 7B ; while minimal computation such as sensor data preprocessing can be performed by the CGR device  104 , as shown in  FIG. 7B . Further as shown in  FIG. 7A , prior to displaying the scene, data from the CGR device  104  (e.g., pose data) can be used for final correction. In some embodiments, the frames sent by the base for rendering are timestamped. Knowing the time when the frames are computed at the base, upon receiving the frames from the base, the CGR device predicts a rendering pose at a rendering time based on a trajectory of the pose data (e.g., corresponding to a current posture of the user as measured by the image acquisition interface). The CGR device then performs final correction by adjusting the frames using the rendering pose at the rendering time. 
     In some embodiments, as represented by block  856 , the method  800  includes determining the access schedule and/or the arrangement by a second device. In particular, the distributed access schedule determination includes the steps of triggering collection of data by a base device, wherein the base device obtains at least one of the arrangement for the image data, the pose data, and the gaze estimation, the plurality of characteristics of the plurality of processes, and the hardware timing parameters from the device, and the data are also collected by the base device from other devices; and receiving the access schedule from the second device, wherein the access schedule is determined by the second device based on the data. For example, as shown in  FIG. 7B , the controller  102  obtains processes characteristics, data arrangement information, and hardware parameters etc. from the CGR device  104 . In some embodiments, the controller  102  also receives such information from other CGR devices. Utilizing the collected information, the controller  102  provides the system wide access schedule  554 - 1  and/or data arrangement  552 - 1 . 
     In some embodiments, the method  800  continues, in block  860 , with the device accessing the arrangement according to the access schedule in order to generate a scene for display (e.g., a CGR scene that is a representation of a real-world scene or a CGR scene that is a fully virtual scene); and displaying the scene using data obtained from the arrangement and according to the access schedule, where the data is a subset of at least one of the image data, the pose data, and the gaze estimation. For example, in  FIG. 7A , the scene provided to the display  612  of the CGR device is generated using the data from the buffer  720 . As shown in  FIG. 4 , the processes  410  for producing the scene pulls the data stream from the buffer  425  according to the access schedule, where data  427  in the buffer  425  are arranged according to the arrangement. In another example, as shown in  FIG. 5B , the scene recognition process  560 , the ROI recognition process  562 , and the object recognition process  564  access data according to the access schedule  554 , and the ROI image data  590  and the non-ROI image data  592  are stored according to the data arrangement  552 . 
     In some embodiments, as represented by block  862 , accessing the arrangement according to the access schedule includes determining an expected execution time for a process of the plurality of processes based on the access schedule and waking up the process for data access at the expected execution time upon fetching data used by the process according to the arrangement. For example, as shown in  FIG. 3 , based on the access schedule, the expected execution time for process M at time T 4  is determined according to the access scheduled. At time T 4 , the image data from the camera are fetched by the process M from a buffer, in which data are arranged according to the system determined data arrangement. 
     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: 20190822
Publication Date: 20211221
Grant Date: 20211221
Priority Date: 20180823
Inventors: DESAI, RANJIT
ROCKWELL, Michael J.
DUGGINENI, VENU MADHAV
LEE, Robert Seon Wai
Assignee: APPLE INC
CPC Classifications: [{"code": "G06F9/5038", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/4881", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/038", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/23", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/166", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/5038", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/4887", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/4881", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/451", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/038", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0346", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06V40/166", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/23", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/451", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0346", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/166", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/4887", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/451", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F9/4887", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/038", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/451", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06V40/23", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/451", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06K9/00255", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06K9/00342", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 67841287