Distributed Pose Prediction for Low-Latency XR Rendering
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Solution Overview
Problem
The computational complexity of pose estimation systems imposes significant power and resource demands, limiting the performance and scalability of extended reality (XR) applications, particularly in mobile and wearable devices.
Innovation Solution
A distributed XR system is implemented, where a client device transmits pose prediction parameters to a server device, which predicts poses based on these parameters and generates virtual content, reducing computational burden on the client and enhancing accuracy by predicting poses just before rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pose estimation is performed locally on the XR device, then real-time tracking is achieved, but power consumption and computational resource demands increase significantly
Solution Approach 1:
The system divides the computational workload between the XR device and a remote server. The XR device performs lightweight pose prediction using IMU data and transmits only essential parameters, while the server handles computationally intensive virtual content generation and rendering. This segmentation reduces local power consumption while maintaining real-time tracking capability.
Solution Approach 2:
A remote server acts as an intermediary between the XR device and the virtual content generation process. The server receives pose prediction parameters from the device, performs complex computations for content generation, and returns rendered content to the device. This intermediary approach offloads computational tasks from the battery-powered XR device.
2Use of energy by moving object
If pose estimation computational complexity is reduced, then power consumption decreases, but tracking accuracy and performance deteriorate
Solution Approach 1:
The system performs preliminary pose prediction using IMU data and prediction coefficients before virtual content generation. By predicting poses in advance and transmitting only prediction parameters rather than full pose estimation results, the system reduces computational complexity while maintaining sufficient accuracy for rendering purposes.
Solution Approach 2:
The system uses different computational approaches for different purposes: lightweight IMU-based prediction for real-time tracking on the device, and more accurate camera-based pose estimation on the server when available. Each component performs pose estimation with appropriate complexity for its specific function and resource constraints.
3Reliability
If more accurate pose prediction is implemented, then visual artifacts are reduced, but computational resource demands increase
Solution Approach 1:
The system dynamically adjusts prediction parameters such as prediction time offsets and coefficient updates based on motion characteristics. During high-motion periods, the system increases prediction frequency and complexity to maintain accuracy, while during stable periods, it reduces computational effort. This dynamic adaptation maintains visual quality while optimizing resource usage.
Data Source
AI summary
Systems and techniques are described herein for generating and/or processing virtual content in a distributed system (e.g., a distributed extended reality system). For example, a first device (e.g., a client device) of a distributed extended reality system may transmit one or more sets of pose prediction parameters (e.g., prediction coefficients, prediction time associated with raw pose data, and/or the raw pose data) to a second device (e.g., a server device) of the distributed extended reality system. The second device may predict one or more poses of the second device based on the set(s) of pose prediction parameters, and may generate virtual content based on a pose of the predicted pose(s) of the first device. The second may transmit and the first device may receive the virtual content. The first device may then display one or more virtual objects based at least in part on the received virtual content.