XR Pose Correction via Server Feedback Loop
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In extended reality (XR) environments, pose correction accuracy is often compromised due to factors like the capabilities of reprojection algorithms, differences between predicted and final poses, scene complexity, and occlusions/disocclusions, leading to unsatisfactory visual results.
Innovation Solution
A method and apparatus for improving pose correction accuracy in XR by predicting a user's pose for an estimated display time, sending this prediction to a server, receiving a rendered image and pose from the server, determining a final pose, reprojecting the image, and sending a metric related to the image quality as feedback to the server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pose correction is performed using traditional reprojection algorithms, then rendering performance is maintained, but pose correction accuracy deteriorates due to differences between predicted and final poses
Solution Approach 1:
The system implements a feedback mechanism where the client device sends pose prediction data to the server, receives rendered images, compares them with reprojected images using metrics like PSNR or SSIM, and feeds this information back to improve future reprojection operations. This feedback loop enables continuous optimization of pose correction accuracy without significantly increasing computational complexity.
Solution Approach 2:
The system performs preliminary pose prediction for an estimated display time before actual rendering occurs. By predicting the pose in advance and using this prediction for pre-rendering, the system reduces the time gap between prediction and actual display, thereby improving pose correction accuracy while maintaining rendering performance.
2Measurement precision
If reprojection algorithms are enhanced to improve pose correction accuracy, then visual quality improves, but processing time increases
Solution Approach 1:
The server performs rendering in advance based on predicted poses, creating a buffer of pre-rendered frames. This preliminary rendering action allows the system to reduce real-time processing requirements during actual display, as the heavy computational workload is shifted to the prediction and pre-rendering phase, thereby improving accuracy without significantly increasing perceived processing time.
Solution Approach 2:
The feedback mechanism allows the system to learn from previous reprojection errors and optimize future rendering operations. By analyzing metrics like PSNR and SSIM from previous iterations, the system can adjust its reprojection parameters to achieve higher accuracy with reduced computational overhead, effectively trading processing time for improved visual quality in a controlled manner.
3Measurement precision
If feedback mechanism is implemented to improve reprojection quality, then pose correction accuracy improves, but system complexity increases
Solution Approach 1:
The feedback mechanism operates through a relatively simple protocol where the client sends pose prediction data to the server, receives rendered images back, computes quality metrics (PSNR, SSIM), and feeds this information back to improve future operations. This straightforward feedback loop improves pose correction accuracy without introducing complex control systems or additional hardware requirements.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The embodiments relate to a client and a server for a pose correction. The client performs the following: predicting a pose of a user for an estimated display time; sending the predicted pose to a server; receiving from the server a rendered image and a rendered pose, wherein the rendered pose corresponds to the predicted pose; determining a final pose, wherein the final pose corresponds to an estimated pose of the user at a time when the image is displayed to the user; reprojecting the rendered image using the final pose; and means for determining a metric relating to a received image and a reprojected image, and sending the metric as a feedback message. The server renders an image for a rendered pose, wherein the rendered pose corresponds to the predicted pose received from the client.