Controller Future Pose Prediction for XR Latency Compensation
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Solution Overview
Problem
Extended reality systems experience latency issues due to data transfer and rendering delays, leading to a mismatch between the rendered visual data and the actual movement of controllers, which degrades the user experience.
Innovation Solution
A future pose predictor using a pose estimation engine that predicts controller poses based on current and past poses, without requiring velocity or acceleration data, to align rendered images with actual controller movements, reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wireless controllers are used to provide easy and comfortable user experience, then ease of operation is improved, but data transfer latency increases
Solution Approach 1:
The system performs preliminary actions by predicting future controller poses before the actual rendering occurs. The pose prediction engine estimates where the controller will be at future time points based on current and historical pose data, allowing the rendering system to prepare visual content in advance. This preliminary prediction compensates for the inherent latency in wireless data transfer, ensuring that when the rendered image is finally displayed, it corresponds to the actual controller position at that moment rather than an earlier position.
2Measurement precision
If data processing is performed to generate visual data from raw IMU pose data, then visual accuracy is improved, but rendering latency increases
Solution Approach 1:
The system performs preliminary pose prediction calculations based on current and historical IMU data to estimate future controller positions. This allows the rendering pipeline to begin processing visual data in advance using predicted poses, rather than waiting for complete processing of current poses. The prediction engine pre-computes where the controller will be, enabling the rendering system to prepare frames ahead of time, thus reducing overall rendering latency while maintaining visual accuracy through continuous prediction updates.
3Loss of time
If pose prediction is performed to reduce latency, then time synchronization is improved, but computational complexity increases
Solution Approach 1:
The system introduces a pose prediction engine as an intermediary component between the IMU data acquisition and the rendering pipeline. This intermediary layer processes raw pose data and generates predicted future poses that the rendering system can use. By placing this prediction intermediary, the system achieves time synchronization without requiring complex modifications to the existing IMU sampling or rendering pipelines. The prediction engine acts as a buffer that translates current historical data into future predictions, simplifying the overall system architecture while reducing latency.
Data Source
AI summary
Systems and techniques are described herein for predicting poses. An apparatus for predicting a pose includes at least one memory and at least one processor coupled to at least one memory and configured to: receive, at a pose estimation engine, pose data from a controller, the pose data including a plurality of previous poses of the controller; and predict, at a first time via the pose estimation engine, a future pose of the controller based on the pose data, the future pose comprising an expected pose of the controller at a second time that is after the first time.