Method and device sharing process data

Abstract

To provide a system, a method and a device for sharing data with low delay using a deterministic pull / fetch model.SOLUTION: A method for accessing shared data between processes includes: acquiring image data acquired by an image acquisition interface; determining pose data, at least partially on the basis of inertial measurement unit (IMU) information measured by the image acquisition interface; determining line-of-sight estimation, at least partially on the basis of eye-tracking information acquired via the image acquisition interface; determining a configuration of image data, pose data and line-of-sight estimation, at least partially on the basis of characteristics of the processes; and determining an access schedule of the process, at least partially on the basis of at least one of the configuration, the characteristics of the process and a hardware timing parameter related to a device.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The present disclosure relates generally to data sharing, and more particularly to systems, methods, and devices that provide low latency data sharing using a deterministic pull / fetch model. [Background technology]

[0002] The physical environment refers to the physical world that people can sense and / or interact with without the aid of electronic systems. A physical environment, such as a physical park, includes physical objects, such as physical trees, physical buildings, and physical people. People can sense and / or interact with the physical environment directly, such as through sight, touch, hearing, taste, and smell.

[0003] In contrast, a computer-generated reality (CGR) environment refers to a wholly or partially simulated environment that people sense and / or interact with via electronic systems. In a CGR, a subset of a person's physical movements or representations thereof are tracked, and in response, one or more properties of one or more virtual objects simulated within the CGR environment are adjusted to conform to at least one law of physics. For example, a CGR system may detect that a person's head is rotating and, in response, adjust the graphical content and sound field presented to the person in a manner that resembles how such sights and sounds would change in a physical environment. In some circumstances (e.g., for ease of use reasons), adjustment of a property(s) of a virtual object(s) in a CGR environment may be made in response to a representation of a physical movement (e.g., a voice command).

[0004] A person may sense and / or interact with a CGR object using any one of these senses, including vision, hearing, touch, taste, and smell. For example, a person may sense and / or interact with an audio object that creates an audio environment with 3D or spatial breadth that provides the perception of a point sound source in 3D space. In another example, an audio object may enable audio transparency that 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 with only audio objects.

[0005] Examples of CGR include virtual reality and mixed reality.

[0006] A virtual reality (VR) environment refers to a simulated environment designed to be based entirely on computer-generated sensory input for one or more senses. A VR environment includes a number of virtual objects that a person can sense and / or interact with. For example, computer-generated images of trees, buildings, and avatars representing people are examples of virtual objects. A person can sense and / or interact with virtual objects in a VR environment through a simulation of the person's presence in the computer-generated environment and / or through a simulation of a subset of the person's physical movements in the computer-generated environment.

[0007] In contrast to a VR environment, which is designed to be based entirely on computer-generated sensory input, a mixed reality (MR) environment refers to a simulated environment that is designed to incorporate sensory input from, or representations of, the physical environment in addition to including computer-generated sensory input (e.g., virtual objects). On the continuum of virtuality, a mixed reality environment lies somewhere between, but not including, a fully physical environment at one end and a virtual reality environment at the other end.

[0008] In some MR environments, computer-generated sensory inputs may respond to changes in sensory inputs from the physical environment. Also, some of the electronic systems for presenting MR environments may track location and / or orientation with respect to the physical environment to allow virtual objects to interact with real objects (i.e., physical items or representations thereof from the physical environment). For example, the system may be responsible for the movement of a virtual tree so that it appears stationary relative to the physical ground.

[0009] Examples of mixed reality include augmented reality and augmented virtual reality.

[0010] An augmented reality (AR) environment refers to a simulated environment in which one or more virtual objects are overlaid on a physical environment or a representation thereof. For example, an electronic system for presenting an AR environment may have a transparent or semi-transparent display through which a person can directly view the physical environment. The system may be configured to present virtual objects on the transparent or semi-transparent display, whereby a person uses the system to perceive the virtual objects overlaid on the physical environment. Alternatively, the system may have an opaque display and one or more imaging sensors that capture images or videos of the physical environment, which are representations of the physical environment. The system composites the images or videos with the virtual objects and presents the composite on the opaque display. A person uses the system to indirectly view the physical environment through the images or videos of the physical environment and perceive the virtual objects overlaid on the physical environment. As used herein, the video of the physical environment shown on the opaque display is referred to as "pass-through video," meaning that the system uses one or more imaging sensors to capture images of the physical environment and uses those images in presenting the AR environment on the opaque display. Further alternatively, the system may include a projection system that projects virtual objects into the physical environment, for example as a hologram or onto a physical surface, so that a person using the system perceives the virtual objects as overlaid on the physical environment.

[0011] Augmented reality environments also refer to simulated environments 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 be imposed with a selected perspective (e.g., viewpoint) that differs from the perspective captured by the imaging sensor. As another example, the representation of the physical environment may be transformed by graphically altering (e.g., magnifying) a portion thereof, such that the altered portion is a representative but less realistic version of the original captured image. As a further example, the representation of the physical environment may be transformed by graphically removing or obscuring a portion thereof.

[0012] 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 a 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 have their faces realistically reproduced from images taken of physical people. As another example, virtual objects may adopt the shape or color of physical items imaged by one or more imaging sensors. As a further example, virtual objects may adopt shadows consistent with the position of the sun in the physical environment.

[0013] There are many different electronic systems that allow a person to sense and / or interact with various CGR environments, including, for example, smartphones, tablets, desktop / laptop computers, head-mounted systems, projection-based systems, heads-up displays (HUDs), vehicle windshields with integrated viewing capabilities, windows with integrated viewing capabilities, displays formed as lenses designed to be placed against a person'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 with hand tracking and / or other body pose estimation capabilities).

[0014] The head-mounted system may have one or more speaker(s) and an integrated opaque display. Alternatively, the 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 for capturing images or video of the physical environment and / or one or more microphones for capturing audio of the physical environment. The head-mounted system may have a transparent or translucent display rather than an opaque display. The transparent or translucent display may comprise a medium through which light representing an image is directed to the human eye. The display may utilize digital light projection, OLED, LED, uLED, liquid crystal on silicon, laser scanning light source, or any combination of these technologies. The medium may be a light guide, a holographic medium, an optical coupler, an optical reflector, or any combination of these. In one implementation, the transparent or translucent display may be configured to be selectively opaque. The projection-based system may employ retinal projection technology that projects a graphic image onto the human retina. The projection system may also be configured to project virtual objects into the physical environment, for example as holograms or onto physical surfaces.

[0015] Content available on CGR devices is becoming increasingly immersive, more graphic intensive, and universally applicable to everyday life. Thus, the hardware in CGR devices continues to evolve to accommodate resource-intensive processes and keep up with CGR content. However, as multiple processes simultaneously compete for resources, latency and a large number of interrupts inevitably create a bottleneck effect. Visible latency issues can negatively impact the user's experience. [Brief description of the drawings]

[0016] The present disclosure may have a more detailed description by reference to aspects of several exemplary implementations, some of which are illustrated in the accompanying drawings, as can be understood by those skilled in the art.

[0017] [Figure 1] FIG. 1 is a block diagram of an exemplary operating environment, according to some implementations.

[0018] [Diagram 2] FIG. 1 is a block diagram illustrating an interruption model for CGR data sharing, according to some implementations.

[0019] [Diagram 3] FIG. 1 is a block diagram of an example deterministic pull / fetch model for CGR data sharing between processes, according to some implementations.

[0020] [Figure 4] FIG. 1 is a block diagram illustrating an example CGR data sharing process based on a deterministic pull / fetch model, according to some implementations.

[0021] [Figure 5A] FIG. 2 is a block diagram of an example of a controller used in a pull / fetch model, according to some implementations.

[0022] [Figure 5B] FIG. 2 is a block diagram illustrating an example controller managing data configuration and process access scheduling, according to some implementations.

[0023] [Figure 6] FIG. 1 is a block diagram of an example CGR device, according to some implementations.

[0024] [Figure 7A] 1 illustrates a CGR pipeline according to several implementations.

[0025] [Figure 7B] 1 illustrates a distributed system that implements a CGR pipeline, according to some implementations.

[0026] [Figure 8] 1 is a flowchart of a method for accessing shared data between processes according to some implementations.

[0027] According to common practice, the various features illustrated in the figures may not be drawn to scale. Thus, dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some drawings may not depict all of the components of a given system, method, or device. Finally, like reference numbers may be used throughout the specification and figures to indicate similar features. Summary of the Invention

[0028] Various implementations disclosed herein include devices, systems, and methods for accessing shared data between processes. In various implementations, the method is executed in a device including one or more processors, a non-transitory memory, and an image capture interface. The method includes acquiring image data associated with a field of view acquired by the image capture 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 capture interface. The method further includes determining a gaze estimation based at least in part on eye tracking information acquired via the image capture interface. The method also includes determining the image data, the pose data, and a configuration for gaze estimation based at least in part on a plurality of characteristics of a plurality of processes in communication with the image capture 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 image data, the pose data, and a configuration for gaze estimation, the plurality of characteristics of the plurality of processes, and hardware timing parameters associated with the device.

[0029] According to some implementations, the device includes one or more processors, a non-transitory memory, and one or more programs. The one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors, the one or more programs including instructions to perform or cause to be performed any of the methods described herein. According to some implementations, a non-transitory computer-readable storage medium has instructions stored therein that, when executed by one or more processors of the device, perform or cause the device to perform any of the methods described herein. According to some implementations, the device includes one or more processors, a non-transitory memory, and means for performing or causing to be performed any of the methods described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] Numerous details are described to provide a thorough understanding of the exemplary implementations shown in the drawings. However, the drawings merely illustrate some exemplary aspects of the present disclosure and therefore should not be considered limiting. Those skilled in the art will understand that other effective aspects and / or variations 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 exemplary implementations described herein.

[0031] As mentioned above, processes on a CGR device that follow a resource interrupt model often compete for resources simultaneously. As a result, bottleneck effects can cause delays that negatively impact the user's experience. Various implementations disclosed herein move away from the interrupt model to a deterministic pull / fetch model. An access schedule is determined that allows a process to fetch data at a set time. The access schedule is determined based on known (e.g., deterministic) information, such as system parameters and user pause information. Thus, various implementations described herein address the above-mentioned shortcomings, specifically those involving data access. As a result, less memory and processing resources are consumed. Not surprisingly, the overall user experience can be improved due to reduced delays as data passes through the CGR display pipeline.

[0032] 1 is a block diagram of an exemplary operating environment 100 according to some implementations. While relevant features are shown, those skilled in the art will appreciate from this disclosure that various other features are not shown for brevity's sake so as not to obscure more pertinent aspects of the exemplary embodiments disclosed herein. To that end, as a non-limiting example, the operating environment 100 includes a controller 102 and a CGR device 104.

[0033] In some implementations, the CGR device 104 corresponds to a tablet or a 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) into which the tablet or mobile phone is inserted. 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.

[0034] According to some implementations, the CGR device 104 presents CGR content to the user via the display 122 while the user is virtually and / or physically present in a scene 106 that includes a table 107 in 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., a virtual cylinder 109) and enable video pass-through of the scene 106 (e.g., including a representation 117 of the table 107) on the display 122. In some implementations, the CGR device 104 is configured to present virtual content and enable optical see-through of the scene 106.

[0035] In some implementations, a user holds the CGR device 104 in their hand(s). In some implementations, a user wears the CGR device 104 on their head. The CGR device 104 thus includes one or more CGR displays provided for displaying CGR content. For example, the CGR device 104 surrounds the user's field of view. In some implementations, the CGR device 104 is configured to present CGR content and replaces a CGR chamber, enclosure, or room in which the user does not wear the CGR device 104.

[0036] In some implementations, the controller 102 is configured to manage and coordinate the presentation of CGR content to 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 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 (e.g., a cloud server, a central server, etc.) located outside the scene 106. In some implementations, the controller 102 is communicatively coupled to 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 functionality of the controller 102 is provided by and / or combined with the CGR device 104.

[0037] As shown 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 differs from the scene 106 in which 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, to generate the representation of the scene 106, the controller 102 and / or the CGR device 104 modify (e.g., augment) an image 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.

[0038] FIG. 2 is a block diagram illustrating an interrupt model 200 for CGR data sharing. In some implementations, to generate a representation of the scene 106 (as shown in FIG. 1), multiple processes, e.g., process 1 210-1, process 2 210-2, process 3 210-3, ... process N 210-N, take as input raw data obtained by the CGR device 104 (as shown in FIG. 1). For example, the raw data includes image data 230 obtained by an image sensor(s), pose data 240 obtained by an IMU, gaze estimation data 250 derived from information obtained by an eye tracking device, and other data 260 obtained by the CGR device 104. In some implementations, the multiple processes 210 are interdependent such that output from one process is used as input by another process, e.g., output from process 2 210-2 is an input to process 3 210-3. In some implementations, raw data and / or output from process 210 is communicated through a communication path 220, e.g., a communication path established via a communication interface(s) of controller 102 and / or a communication interface(s) of CGR device 104.

[0039] 2, if process 1 210-1 needs image data 230 and gaze estimation data 250, process 1 210-1 interrupts the image sensor and eye tracking device to obtain a copy of image data 230 and a copy of gaze estimation data 250 as input. Similarly, if process 2 210-2 also needs image data 230 and gaze estimation data 250, process 2 210-2 interrupts the image sensor and eye tracking device to obtain a copy of image data 230 and a copy of pose estimation data 250 as input. In another example, as shown in FIG. 2, process 3 210-3 needs input from process 2 210-2, pose data 240, and other data 260. Process 3 210-3 waits for the completion of process 2 210-2 while interrupting the IMU and other sensor(s) to obtain pose data 240 and other data 260.

[0040] 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 competing for resources (e.g., image data 230 and gaze estimation data 250), the interrupt created multiple bottlenecks, e.g., at least one bottleneck at the sensor and another at the communication path 220. Second, the interrupt model 200 does not share memory across different tasks because multiple copies of the data are created for multiple processes. Thus, memory usage is inefficient. Due to the cascading effects from bottlenecks, inefficient memory utilization, and process dependencies, the interrupt model 200 cannot meet the latency requirements for real-time streaming of CGR content. Thus, CGR scenes presented using the interrupt model 200 may induce motion sickness in the user.

[0041] FIG. 3 is a block diagram of an exemplary deterministic pull / fetch model 300 for CGR data sharing between processes, according to some embodiments. A deterministic system typically does not involve randomness in the evolution of the future state of the system. As such, a deterministic model produces the same output from a given starting condition or initial state. Thus, using the deterministic model 300, the system can predict when data will be generated and from where it will be accessed. Thus, the pull / fetch model 300 allows data to be retrieved from an efficient location for retrieval when the data is ready to be fetched. Hence, bottlenecks and resource contention are reduced in such a system.

[0042] For example, FIG. 3 shows two processes, process M and process N, where process N depends on the output from process M, and process M fetches image data captured by a camera. The system learns hardware timing parameters, such as the camera exposure time Δ1, as well as characteristics of the processes, such as the processing time Δ2 of 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 the processes fetch data and where the data is fetched from. In FIG. 3, the system uses a system synchronous clock to measure the start time (e.g., T1), the expected camera operation duration (e.g., T2-T3), and the expected process M execution time (T4-T5), and calculates the wake-up time T4 of process M and the wake-up time T6 of process N.

[0043] In some embodiments, the deterministic system generates an access schedule for the processes that includes a calculated wake-up time for each process. In some embodiments, a threshold amount of time is reserved between each task, e.g., between T1 and T2, between T3 and T4, and between T5 and T6. The threshold amount of time is reserved for communication delays between the processes and / or hardware components. For example, the time between T1 and T2 is for the system to notify the camera to start acquiring image data, the time between T3 and T4 is for the camera to wake up process M, and the time between T5 and T6 is for process M to wake 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 shown in FIG. 2, the pull / fetch model 300 as shown in FIG. 3 reduces constraints at the system level and improves system performance overall.

[0044] 4 is a block diagram illustrating a CGR data sharing process 400 based on a deterministic pull / fetch model 300 according to some implementations. In some implementations, to generate a representation of the scene 106 (as shown in FIG. 1), multiple 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 shown in FIG. 1). For example, the raw data includes image data 430 acquired by the image sensor(s), pose data 440 acquired by the IMU, gaze estimation data 450 derived from information acquired by an eye tracking device, and other data 460 acquired by the CGR device 104. In some implementations, the multiple processes are interdependent such that output from one process is used as input by another process, e.g., output from process 2 410-2 is input to process 3 410-3. In some implementations, raw data and / or output from multiple processes 410 are communicated through a communication path 420, e.g., a communication path established via a communication interface(s) of the controller 102 and / or a communication interface(s) of the CGR device 104.

[0045] 4, using the deterministic model 300, the controller 102 directs the data organization (e.g., layout and / or sequencing) of the data 427 in the buffer 425 and wakes up the multiple processes 410 according to an access schedule, e.g., when the data in the buffer 425 is ready to be pulled / fetched. For example, image data 427-2 of process 2 410-2 (represented as (I,2) in FIG. 4) and image data 427-4 of process 3 410-3 (represented as (I,3) in FIG. 4) are obtained from the camera-captured image data 430. Image data 427-2 from process 2 410-2 and image data 427-4 from process 3 410-3 are arranged in buffer 425 in a layout and / or sequence such that when image data 427-2 and 427-4 are provided to process 410 in the data stream, process 2 410-2 and process 3 410-3 wake up in a sequence according to the access schedule. Specifically, when process 3 410-3 wakes up, image data 427-4 is ready to be pulled / fetched as well as the output from process 2 410-2.

[0046] Figure 5A is a block diagram of an example of a controller 102 used in the pull / fetch model described above, according to some implementations. Figure 5B is a block diagram illustrating data organization and process access scheduling managed by the controller 102, according to some implementations. Figure 5B is used to illustrate an example CGR scene generation managed by the controller 102 shown in Figure 5A, according to some implementations. While certain features are shown, those skilled in the art will appreciate from this disclosure that various other features are not shown for the sake of brevity so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, and by way of non-limiting example, as shown in FIG. 5A , in some implementations, the controller 102 may include one or more processing units 502 (e.g., a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a graphics processing unit (GPU), a central processing unit (CPU), a processing core, etc.), 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 802.1 ... 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 similar type interfaces), one or more programming (e.g., I / O) interfaces 510, memory 520, and one or more communication buses 504 for interconnecting these and various other components.

[0047] In some implementations, the one or more communication buses 504 include circuitry that interconnects and controls communications between the 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, etc.

[0048] 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 (DDRRAM), 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 storage devices. The memory 520 optionally includes one or more storage devices located remotely from the one or more processing units 502. The memory 520 includes 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:

[0049] The operating system 530 includes procedures for handling various basic system services and for executing hardware-dependent tasks. In some implementations, the CGR content module 540 is configured to manage and coordinate the 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 acquisition unit 542, a tracking unit 544, a coordination unit 546, a data transmission unit 548, and a data access unit 550.

[0050] In some implementations, the data acquisition unit 542 is configured to acquire data (e.g., image data, pose data, gaze estimation, presentation data, interaction data, sensor data, position data, etc.) from at least the CGR device 104. To that end, in various implementations, the data acquisition unit 542 includes instructions and / or logic therefor, as well as heuristics and metadata therefor.

[0051] In some implementations, the tracking unit 544 is configured to map the scene 106 and track at least the position / location of 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, as well as heuristics and metadata therefor.

[0052] In some implementations, the coordination unit 546 is configured to manage and coordinate the presentation of CGR content to users by the CGR device 104. 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. This information is then used by the coordination unit 546 to coordinate processes at a system level. To that end, in various implementations, the coordination unit 546 includes instructions and / or logic therefor, as well as heuristics and metadata therefor.

[0053] In some implementations, the data transmission 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 transmission unit 548 includes instructions and / or logic therefor, as well as heuristics and metadata therefor.

[0054] In some implementations, the data access unit 550 is configured to determine an access schedule that allows a process to fetch data acquired from the data acquisition unit 542. In some implementations, the data access unit 550 determines a data structure 552 for efficient data retrieval by the process. In some implementations, the data access unit 550 also determines an access schedule 554 for a process to pull or fetch data. To that end, in various implementations, the data transmission unit 548 includes instructions and / or logic therefor, as well as heuristics and metadata therefor.

[0055] For example, in FIG. 5B, a scene recognition process 560 obtains image data and pose data 580 for recognizing a scene 106. When an eye tracking device detects that a user has fixed their gaze proximate to a region of interest (ROI) 572 in the scene 106, a ROI recognition process 562 identifies ROI image data 590 in the image data based on gaze estimation data 582. An object recognition process 564 then analyzes the ROI image data 590 and recognizes an object 574 in the ROI 572. As shown in FIG. 5B, processes 560, 562, and 564 access data structure 552 according to an access schedule 554, where the access schedule 554 includes information such as the start time of each process. Also, as shown in FIG. 5B, the data in data structure 552 is configured to accommodate scheduled fetching. For example, because ROI image data 590 is used by ROI recognition process 562 and object recognition process 564, ROI image data 590 may be stored separately from non-ROI image data 592 to accommodate more frequent or more urgent access.

[0056] Referring to FIG. 5A above, the data acquisition unit 542, the tracking unit 544, the adjustment unit 546, the data transmission unit 548, and the data access unit 550 are shown as being present on a single device (e.g., the controller 102), however, it should be understood that in other implementations, any combination of the data acquisition unit 542, the tracking unit 544, the adjustment unit 546, the data transmission unit 548, and the data access scheduling unit 550 may be located within separate computing devices.

[0057] Furthermore, Figure 5A is intended as a functional description of various features present in a particular embodiment, rather than as a structural schematic diagram of the implementations described herein. As will be appreciated by those skilled in the art, items shown separately can be combined and some items can be separated. For example, some functional modules shown separately in Figure 5A can be implemented within a single module, and various functions of a single functional block can be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of specific functions and how functions are allocated among them will vary from embodiment to embodiment, and in some implementations will depend in part on the particular combination of hardware, software, or firmware selected for a particular embodiment.

[0058] 6 is a block diagram of an example CGR device 104, according to some implementations. While certain features are shown, those skilled in the art will appreciate from this disclosure that various other features are not shown for the sake of brevity so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, by way of non-limiting example, in some implementations, the CGR device 104 includes one or more processing units 602 (e.g., a microprocessor, an ASIC, an FPGA, a GPU, a CPU, a processing core, etc.), one or more input / output (I / O) devices 606, one or more communications interfaces 608 (e.g., USB, FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, infrared, BLUETOOTH, ZIGBEE, and / or similar types of interfaces), 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 inward-facing and / or outward-facing image sensors), memory 620, and one or more communications buses 604 for interconnecting these and various other components.

[0059] In some implementations, the one or more communication buses 604 include circuitry that interconnects and controls communications between the 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., a blood pressure monitor, a heart rate monitor, a blood oxygen sensor, a blood glucose sensor, etc.), one or more microphones, one or more speakers, a haptic engine, and / or the like.

[0060] 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 transistor (OLET), organic light emitting diode (OLED), surface conduction electron emitter display (SED), field emission display (FED), quantum dot light emitting diode (QD-LED), microelectromechanical system (MEMS), and / or similar display types. In some embodiments, the one or more CGR displays 612 correspond to a waveguide display, such as diffractive, reflective, polarizing, holographic, etc. For example, the CGR device 104 includes a single AR / VR display. In another example, the CGR device 104 includes a CGR display for both eyes of the user.

[0061] In some implementations, the one or more image capture interfaces 614 are configured to acquire data for CGR content generation. In some implementations, the one or more image capture interfaces 614 include at least one of one or more image sensors, inertial measurement units (IMUs), accelerometers, gyroscopes, thermometers, one or more depth sensors (e.g., structured light, time of flight, etc.), eye tracking devices. For example, the one or more image sensors correspond to one or more RGB cameras (e.g., with a complementary metal oxide semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor), infrared cameras, event-based cameras, etc.

[0062] 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 located remotely from the one or more processing units 602. The memory 620 includes 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:

[0063] The operating system 630 includes procedures for handling various basic system services and for executing hardware-dependent tasks. In some implementations, the CGR presentation module 640 is configured to present CGR content to a user via one or more CGR displays 612. To that end, in various implementations, the CGR presentation module 640 includes a data acquisition unit 642, a CGR presentation unit 644, an eye tracking unit 646, and a data transmission unit 648.

[0064] In some implementations, the data acquisition unit 642 is configured to acquire data (e.g., image data, pose data, presentation data, interaction data, sensor data, position data, etc.) from at least the controller 102. To that end, in various implementations, the data acquisition unit 642 includes instructions and / or logic therefor, as well as heuristics and metadata therefor.

[0065] In some implementations, the CGR presentation unit 644 is configured to present CGR content via one or more CGR displays 612. To that end, in various implementations, the CGR presentation unit 644 includes instructions and / or logic therefor, as well as heuristics and metadata therefor. In some implementations, the CGR presentation unit 644 is configured to project an image including emitted light in a first wavelength range through an eyepiece that distorts the light in the first wavelength range. In some embodiments, the CGR presentation unit 644 is configured to project an image including emitted light in a first wavelength range through an eyepiece that reflects and refracts light in the first wavelength range while passing light in a second wavelength range without substantial distortion.

[0066] In some implementations, the eye tracking unit 646 is configured to emit light in a second wavelength range using one or more light sources disposed between the eyepiece and the display and detect light in the second wavelength range using the camera. In various implementations, the one or more light sources illuminate the user's eyes and the camera detects light reflected from the user's eyes. To that end, in various implementations, the eye tracking unit 646 includes instructions and / or logic therefor, as well as heuristics and metadata therefor. In some implementations, the eye tracking unit 646 is configured to emit light in the second wavelength range through the eyepiece and detect light in the second wavelength range reflected by the user's eyes. In some implementations, the eye tracking unit 646 provides a gaze estimation based at least in part on the detected light reflection from the user's eyes.

[0067] In some implementations, the data transmission 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 transmission unit 648 includes instructions and / or logic therefor, as well as heuristics and metadata therefor.

[0068] Although the data acquisition unit 642, the CGR presentation unit 644, the eye tracking unit 646, and the data transmission unit 648 are shown as being present on a single device (e.g., the CGR device 104), it should be understood that in other implementations, any combination of the data acquisition unit 642, the CGR presentation unit 644, the eye tracking unit 646, and the data transmission unit 648 may be located within separate computing devices.

[0069] Furthermore, Figure 6 is intended as a functional description of various features present in certain embodiments, rather than as a structural schematic diagram of the implementations described herein. As will be appreciated by those skilled in the art, items shown separately can be combined and some items can be separated. In some implementations, some functional modules shown separately in Figure 6 can be implemented within a single module, and various functions of a single functional block can be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of specific functions and how functions are allocated among them will vary from embodiment to embodiment, and in some implementations will depend in part on the particular combination of hardware, software, or firmware selected for a particular embodiment.

[0070] Additionally, in some implementations, the functions performed by the controller 102, as shown in Figure 5A, and the CGR device 104, as shown in Figure 6, are allocated between the devices. For example, Figure 7A shows a CGR pipeline 700 that receives data from sensors and generates a representation of the scene 106 (as shown in Figure 1) according to some implementations. Figure 7B shows a system 750 that implements the CGR pipeline 700. The system 750 allocates tasks performed by the controller 102 and / or the CGR device 104 described above between the controller 102 and the CGR device 104.

[0071] As shown in FIG. 7A, in some implementations, the image capture interface 614 (FIG. 6) includes at least an image sensor 710 for outputting image data, an IMU 712 for outputting pose data, an eye tracking device 714 for providing gaze estimation, and one or more other sensors 716 for providing raw data as input to a process for generating the scene 106. Upon receiving the image data, pose data, gaze estimation data, and raw data, the process performed by the controller 102 stores the received data in a buffer (e.g., buffer 425 of FIG. 4 or buffer 720 of FIG. 7A) according to the data configuration 552 as described above. Additionally, the process performed by the controller 102 accesses the buffer 720 according to an access schedule 554. The access schedule 554 is generated according to a deterministic model as described above with reference to FIG. 3.

[0072] In some implementations, the CGR pipeline 700 includes a rendering module 730 that receives CGR content and data from the buffer 720 and renders images on the display 612. In various implementations, the CGR content includes definitions of the geometry of virtual objects, the color and / or texture of virtual objects, images (such as a see-through image of the scene 106), and other information that describes the content to be represented in the rendered image. In some implementations, final corrections are performed before displaying the rendered image. For example, the rendered image is corrected based on pose data to improve the user's experience.

[0073] In some implementations, as shown in FIG. 7B, final correction and other less computationally intensive tasks (e.g., pre-processing of sensor data) are performed in the CGR device. The system 750 of FIG. 7B implements a CGR pipeline 700 according to some embodiments and allocates functions performed by the controller 102 and / or the CGR device 104. Thus, more computationally intensive tasks, such as machine learning, computer vision, and / or 3D rendering, are performed in the controller 102 using one or more processors 756 of the controller 102. The controller 102 then transports the computed images to the CGR device 104 for final correction before display. In some implementations, the transport process includes compression / decompression and communication between the controller 102 and the CGR device 104.

[0074] In some implementations, the one or more processors 756 include a coordination unit 546 and a data access unit 550, which further includes a data configuration 552-1 and an access schedule 554-1 of processes allocated across the system 750. For example, the controller 102 collects process characteristics and hardware parameters from the CGR devices 104. Machine learning, such as neural networks, can be used to extract and weight platform-wide process characteristics, including transport processes, pre-processing of sensor data, and / or final correction, and the like, thereby predicting the sequencing of processes accessing data. The controller 102 can also determine the data configuration 552-1 to accommodate predicted data access based at least in part on the access schedule 554-1 in some implementations. Although FIG. 7B illustrates a system 750 with one controller 102 and one CGR device 104, in some embodiments, multiple CGR devices can be connected to the controller 102. In such an embodiment, machine learning on the controller 102 can be used to predict access schedules for cross-platform processes, including processes across multiple CGR devices (e.g., a multiplayer CGR environment). In some implementations, the controller 102 allocates system-wide data configurations 552-1 and access schedules 554-1 such that each CGR device obtains a copy or subset of the data configurations 552-1 and / or access schedules 554-1 from the controller 102. Data acquired by the image acquisition interface 614 is configured according to the local copy of the data configurations 552-2 and the local copy of the access schedule 554-2, and processes associated with the CGR devices 104 access the configured data according to the schedule.Thus, unlike other systems where each process or device may be optimized for data access, system 750 according to embodiments described herein provides platform-independent scheduling of data access at a system level such that overall latency is reduced.

[0075] 8 is a flowchart of a method 800 for determining an access schedule for a process, according to some implementations. In various implementations, the method 800 is performed by a device including one or more processors, non-transitory memory, and an image acquisition interface (e.g., image acquisition interface 614 of FIG. 6). In some implementations, the method 800 is performed by processing logic, which includes hardware, firmware, software, or a combination thereof. In some implementations, the method 800 is performed by a processor and / or controller (e.g., controller 102 of FIG. 1) executing instructions (e.g., code) stored in a non-transitory computer-readable medium (e.g., memory). Briefly, in some circumstances, method 800 includes utilizing an image capture interface to acquire image data, determine pose data, and determine gaze estimation; determining the image data, pose data, and a configuration of gaze estimation based at least in part on a plurality of characteristics of a plurality of processes in communication with the image capture interface; and determining an access schedule for the plurality of processes based at least in part on at least one of the image data, the pose data, the configuration of gaze estimation, the plurality of characteristics of the plurality of processes, and hardware timing parameters associated with the device.

[0076] Method 800 begins with the device acquiring image data associated with a field of view acquired by an image acquisition interface at block 810. In some embodiments, the image acquisition interface includes an image sensor (e.g., image sensor 710 of FIG. 7A) for acquiring image data associated with the field of view.

[0077] The method 800 continues at block 820 with the device determining pose data based at least in part on the inertial measurement unit (IMU) information, where the pose data corresponds to a current posture of the user as measured by the image capture interface. In some embodiments, the image capture interface includes an IMU (e.g., IMU 712 of FIG. 7A) for measuring the current posture of the user.

[0078] The method 800 continues with the device determining a gaze estimate based at least in part on eye tracking information acquired via the image capture interface, at block 830. In some embodiments, the image capture interface includes an eye tracking device (e.g., eye tracking device 714 of FIG. 7A) for acquiring the eye tracking information.

[0079] The method 800 continues at block 840 with the device determining a configuration for the image data, pose data, and gaze estimation based at least in part on characteristics of a plurality of processes in communication with the image capture interface. For example, as shown in Figure 4, the configuration of data 427 in buffer 425 is determined by controller 102 using deterministic model 300. The deterministic model 300 shown in Figure 3 captures characteristics of the processes (e.g., execution sequence and / or process execution duration) as part of the parameters for determining the configuration of data 427 in buffer 425.

[0080] In some embodiments, determining the configuration includes aggregating data of the different processes and configuring the aggregated data based at least in part on a plurality of characteristics of the plurality of processes, as represented by block 842. For example, determining the configuration of two processes includes steps consisting of: (1) obtaining at least one of first image data, first pose data, and a first gaze estimation for a first process of the plurality of processes, the first process being characterized by a first set of characteristics; (2) obtaining at least one of second image data, second pose data, and a second gaze estimation for a second process of the plurality of processes, the second process being characterized by a second set of characteristics; and (3) aggregating the first image data, first pose data, and first gaze estimation with the second image data, second pose data, and second gaze estimation to generate image data, pose data, and gaze estimation configured at least in part based on the first set of characteristics and the second set of characteristics.

[0081] For example, as shown in FIG. 4, the organization of data 427 in buffer 425 is determined based at least in part on the characteristics of process 410. If output from process 2 410-2 is used as input by process 3 410-3, data 427-2 for process 2 410-2 is organized in the buffer so that it is ready to be fetched by process 2 410-2 before data 427-4 is ready to be fetched by process 3 410-3. In another example, as shown in FIG. 5B, ROI image data 590 is used by more processes than non-ROI image data 592. Thus, according to data organization 552, ROI image data 590 is stored in an area that is accessed more frequently and / or has a more urgent need.

[0082] 8, the method 800 continues at 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 configuration of image data, pose data, and 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, a time parameter for the first process input, one to which the hardware or at least one of the second process provides input, and obtaining input from the hardware or at least one of the second process, and calculating a wake time for the first process based at least in part on the time parameter. For example, as shown in FIG. 3, for process M, a wake time for execution of process M is determined at least in part based on a camera exposure time, e.g., a known Δ1. For process N, the wake time for execution of process M is determined based at least in part on the camera exposure time and the execution duration of process M, eg, known Δ1 and known Δ2.

[0083] In some embodiments, as represented by block 854, the method 800 includes allocating computationally intensive tasks to the base device and performing final corrections at the device to optimize the user experience (e.g., reduce motion sickness). For example, in the CGR pipeline 700 as shown in FIG. 7A, computationally intensive tasks including rendering 730 can be performed by the controller 102 as shown in FIG. 7B, while minimal calculations such as pre-processing of sensor data can be performed by the CGR device 104 as shown in FIG. 7B. Furthermore, as shown in FIG. 7A, data (e.g., pose data) from the CGR device 104 can be used for final corrections before displaying the scene. In some embodiments, frames sent by the base for rendering are time-stamped. By knowing the time at which the frame was calculated at the base, the CGR device, upon receiving the frame from the base, predicts the rendering pose at the time of rendering (e.g., corresponding to the user's current posture measured by the image acquisition interface) based on the trajectory of the pose data. The CGR device then performs final corrections by adjusting the frame using the rendering pose at the rendering time.

[0084] In some embodiments, as represented by block 856, the method 800 includes determining an access schedule and / or configuration by the second device. Specifically, determining the access schedule after allocation includes steps of triggering collection of data by the base device that obtains at least one of image data, pose data, and configuration of gaze estimation, characteristics of a plurality of processes, and hardware timing parameters from the device, the data also being collected by the base device from other devices, and receiving an access schedule from the second device that is determined by the second device based on the data. For example, as shown in FIG. 7B, the controller 102 obtains process characteristics, data configuration information, hardware parameters, etc. from the CGR device 104. In some embodiments, the controller 102 also receives such information from the other CGR devices. Utilizing the collected information, the controller 102 provides a system-wide access schedule 554-1 and / or data configuration 552-1.

[0085] In some embodiments, the method 800 continues at block 860 with the device accessing the configuration according to the access schedule 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 entirely a virtual scene), using data obtained from the configuration, which is at least a subset of image data, pose data, and gaze estimation, and displaying the scene according to the access schedule. For example, in FIG. 7A, the scene provided to the display 612 of the CGR device is generated using data from the buffer 720. As shown in FIG. 4, the process 410 for generating the scene pulls a data stream from the buffer 425 according to the access schedule, where the data 427 in the buffer 425 is arranged according to the configuration. 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 configuration 552.

[0086] In some embodiments, accessing the configuration according to the access schedule includes determining an expected execution time of a process of the plurality of processes based on the access schedule, and waking up the process to access the data at the expected execution time upon fetching data used by the process according to the configuration, as represented by block 862. For example, as shown in FIG. 3, based on the access schedule, an expected execution time of process M at time T4 is determined according to the scheduled access. At time T4, image data from a camera is fetched by process M from a buffer where the data is placed according to the data configuration determined by the system.

[0087] Although various aspects of implementations within the scope of the appended claims have been described above, it is apparent that various features of the above-described implementations may be embodied in a wide variety of forms, and that the specific structures and / or functions described above are merely illustrative. Based on this disclosure, one skilled in the art should understand that the aspects described herein may be practiced independently of any other aspects, and 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 performed using any number of the aspects described herein. In addition, such an apparatus may be implemented and / or such a method may be performed using structures and / or functions in addition to one or more of the aspects described herein, or other structures and / or functions.

[0088] In this specification, terms such as "first", "second", etc. may be used to describe various elements, but it will be understood that these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first node can be called a second node, and a second node can be called a first node, without changing the meaning of the description, so long as all occurrences of "first node" are consistently renamed, and all occurrences of "second node" are consistently renamed. Although a first node and a second node are both nodes, they are not the same node.

[0089] The terms used herein are for the purpose of describing particular implementations and are not intended to limit the scope of the claims. When used in the description of the implementations to be described and in the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. Also, as used herein, the term "and / or" is understood to refer to and include any and all possible combinations of one or more of the associated listed items. It is further understood that the terms "comprises" and / or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0090] As used herein, the term "if" may be interpreted, depending on the context, to mean "when" or "upon" or "upon determining" or "according to determining" or "upon detecting" that the previously mentioned condition is true. Similarly, the phrases "if [the previously mentioned condition is determined to be true]" or "if [the previously mentioned condition is true]" or "when [the previously mentioned condition is true]" may be interpreted to mean "upon determining" or "upon determining" or "according to determining" or "upon detecting" or "upon detecting" that the previously mentioned condition is true.

Claims

1. 1. A method, in a device having one or more processors, a non-transitory memory, and an image acquisition interface, comprising: acquiring 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, the pose data corresponding to a current posture of a user as measured by the image capture interface; determining a gaze estimation based at least in part on eye tracking information acquired via the image capture interface; and determining a configuration of the image data, the pose data, and the gaze estimation based at least in part on characteristics of a plurality of processes in communication with the image capture interface; determining an access schedule for the plurality of processes based at least in part on at least one of the image data, the pose data, and the configuration of the gaze estimation, the characteristics of the plurality of processes, and hardware timing parameters associated with the device.

2. determining the configuration of the image data, the pose data, and the gaze estimation based at least in part on the characteristics of the processes; obtaining at least one of first image data, first pose data, and a first gaze estimate for a first process of the plurality of processes, the first process being characterized by a first set of characteristics; obtaining at least one of second image data, second pose data, and a second gaze estimate for a second process of the plurality of processes, the second process being characterized by a second set of characteristics; 2. The method of claim 1 , comprising: integrating 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, the image data, the pose data, and the gaze estimation configured at least in part based on the first set of characteristics and the second set of characteristics.

3. The access schedule is determined based on a deterministic model, and the method comprises: determining, for a first process of the plurality of processes, an input to the first process, at least one of hardware or a second process providing the input, and time parameters for obtaining the input from the at least one of the hardware or the second process; and calculating a wake time of the first process based at least in part on the time parameter.

4. Retrieving a frame from a base device for rendering, the frame being associated with a timestamp; predicting a rendering pose at the time of rendering based on the trajectory of the pose data and the timestamp; The method of claim 1 , further comprising adjusting the frame using the rendering pose at the time of rendering.

5. determining the access schedules for the plurality of processes; triggering collection of data by a base device that obtains at least one of the image data, the pose data, and the configuration of the gaze estimation, the characteristics of the processes, and the hardware timing parameters from the device, the data also being collected by the base device from other devices; The method of claim 1 , further comprising: receiving from the second device the access schedule determined by the second device based on the data.

6. accessing the configuration in accordance with the access schedule to generate a scene for display; 6. The method of claim 1 , further comprising: displaying the scene using data obtained from the configuration, the data being a subset of at least one of the image data, the pose data, and the gaze estimation, and according to the access schedule.

7. accessing the configuration according to the access schedule; determining an expected execution time for a process of the plurality of processes based on the access schedule; 7. The method of claim 6, further comprising: upon fetching data used by the process according to the configuration, waking up the process for accessing the data at the expected execution time.

8. 8. The method of claim 1, wherein the image data, the pose data, and the gaze estimation are stored in a storage device, and the configuration of the image data, the pose data, and the gaze estimation includes at least one of a layout or sequencing for storing the image data, the pose data, and the gaze estimation in the storage device.

9. The method of claim 1 , wherein the image capture interface includes an image sensor for capturing the image data associated with the field of view.

10. The method of claim 1 , wherein the image acquisition interface includes an IMU for measuring the current posture of the user.

11. The method of claim 1 , wherein the image capture interface includes an eye tracking device for capturing the eye tracking information.

12. The method of claim 1 , wherein the characteristics of the processes include a sequence of the processes using the image data, the pose data, and the gaze estimation.

13. 13. The method of claim 1, wherein the hardware timing parameters are associated with at least one of an image sensor, an IMU, an eye tracking device, a controller, a processor, or a communication device.

14. A device, comprising: an image acquisition interface; acquiring image data associated with a field of view acquired by the image capture interface; and determining pose data based at least in part on inertial measurement unit (IMU) information, the pose data corresponding to a current posture of a user as measured by the image capture interface; determining a gaze estimation based at least in part on eye tracking information acquired via the image capture interface; determining a configuration of 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 in communication with the image capture interface; the configuration of the image data, the pose data, and the gaze estimation; the plurality of characteristics of the plurality of processes; and one or more processors that determine an access schedule for the plurality of processes based at least in part on at least one of hardware timing parameters associated with the device.

15. the one or more processors: obtaining at least one of first image data, first pose data, and a first gaze estimate for a first process of the plurality of processes, the first process being characterized by a first set of characteristics; obtaining at least one of second image data, second pose data, and a second gaze estimate for a second process of the plurality of processes, the second process being characterized by a second set of characteristics; combining 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 configured based at least in part on the first set of characteristics and the second set of characteristics; The device of claim 14 , further comprising: a processor configured to generate the image data, the pose data, and the gaze estimation based at least in part on the characteristics of the processes.

16. The access schedule is determined based on a deterministic model, and the one or more processors: determining, for a first process of the plurality of processes, an input to the first process, at least one of hardware or a second process providing the input, and time parameters for obtaining the input from the at least one of the hardware or the second process; 16. The device of claim 14 or 15, configured to calculate a wake time of the first process based at least in part on the time parameter.

17. The one or more processors further include: Retrieving the frame associated with the timestamp from the base device for rendering; predicting a rendering pose at the time of rendering based on the trajectory of the pose data and the timestamp; A device according to claim 14 , further comprising a rendering pose at the time of rendering, the rendering pose being used to adjust the frame.

18. the one or more processors: triggering collection of data by a base device that obtains at least one of the image data, the pose data, and the configuration of the gaze estimation, the characteristics of the processes, and the hardware timing parameters from the device, the data also being collected by the base device from other devices; receiving from the second device the access schedule determined by the second device based on the data; The device according to claim 14 , further comprising: a processor configured to determine an access schedule for each of the plurality of processes based on a plurality of access schedules;

19. The one or more processors further include: accessing the configuration in accordance with the access schedule to generate a scene for display; 19. A device according to claim 14, further comprising: a display unit configured to display the scene using data obtained from the configuration, the data being a subset of at least one of the image data, the pose data, and the gaze estimation, and in accordance with the access schedule.

20. the one or more processors: determining an expected execution time for a process of the plurality of processes based on the access schedule; 20. The device of claim 19, further comprising: upon fetching data used by the process according to the configuration, waking up the process for data access at the expected execution time, thereby accessing the configuration according to the access schedule.

21. 21. The device of claim 14, wherein the image data, the pose data, and the gaze estimation are stored in a storage device, and the configuration of the image data, the pose data, and the gaze estimation includes at least one of a layout or sequencing for storing the image data, the pose data, and the gaze estimation in the storage device.

22. 22. The device of claim 14, wherein the image capture interface includes an image sensor for capturing the image data associated with the field of view.

23. 23. A device according to any one of claims 14 to 22, wherein the image acquisition interface includes an IMU for measuring the current posture of the user.

24. 24. The device of claim 14, wherein the image capture interface includes an eye tracking device for capturing the eye tracking information.

25. The device of claim 14 , wherein the characteristics of the processes include a sequence of the processes using the image data, the pose data, and the gaze estimation.

26. 26. The device of claim 14, wherein the hardware timing parameters are associated with at least one of an image sensor, an IMU, an eye tracking device, a controller, a processor, or a communication device.

27. A non-transitory computer-readable medium having encoded thereon instructions that, when executed by one or more processors of a device including an image capture interface, cause the device to: acquiring 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, the pose data corresponding to a current posture of a user as measured by the image capture interface; determining a gaze estimation based at least in part on eye tracking information acquired via the image capture interface; and determining a configuration of 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 in communication with the image capture interface; determining an access schedule for the plurality of processes based at least in part on at least one of the image data, the pose data, and the configuration of the gaze estimation, the characteristics of the plurality of processes, and hardware timing parameters associated with the device. Non-transitory computer-readable medium.

Images