Client-Side Adaptive Mesh Reprojection for Low-Latency 6DOF Rendering
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Solution Overview
Problem
Existing virtual reality applications face latency issues due to remote rendering, which can cause user discomfort when predicted user poses are incorrect, and waiting for corrected frames incurs additional latency.
Innovation Solution
Implementing a 6DOF reprojector that generates an adaptive 3D polygon mesh on the client-side to reproject frames with the correct user pose, using an adaptive mesh density based on scene content and view errors, and filling disocclusion holes with historical frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If cloud rendering is used to render frames remotely, then rendering quality is improved, but latency increases due to transmission time
Solution Approach 1:
The system predicts future user head poses and pre-renders frames for these predicted poses in advance. When the user actually adopts the predicted pose, the pre-rendered frame is immediately displayed without waiting for cloud rendering and transmission, thereby reducing latency while maintaining rendering quality.
Solution Approach 2:
The system dynamically adjusts the rendering strategy based on the accuracy of pose prediction. When prediction is accurate, pre-rendered frames are used; when prediction deviates, the system requests corrected frames from the cloud service, creating a dynamic hybrid approach that optimizes between latency and quality.
2Measurement precision
If waiting for corrected frames from cloud service, then rendering accuracy is improved, but latency increases
Solution Approach 1:
The system continuously monitors the accuracy of pose prediction by comparing predicted poses with actual user head poses. When deviation exceeds a threshold, the system requests corrected frames from the cloud service, creating a feedback loop that maintains rendering accuracy while minimizing latency through selective correction requests.
Solution Approach 2:
Instead of waiting passively for corrected frames, the system proactively predicts which corrections will be needed and prepares alternative frames in advance, reducing the time penalty when corrections are required.
3Productivity
If using adaptive mesh density, then rendering speed is improved, but visual quality may deteriorate
Solution Approach 1:
The system applies different mesh densities to different regions of the scene based on their importance and complexity. High-density meshes are used for foreground objects and regions with high visual importance, while low-density meshes are used for background regions, maintaining visual quality where needed while improving overall rendering speed.
Solution Approach 2:
The mesh density is dynamically adjusted based on factors such as object depth, motion speed, and visual importance. Objects that are closer to the camera or moving faster receive higher mesh density, while distant or static objects use lower density, optimizing the balance between rendering speed and visual quality in real-time.
Data Source
AI summary
A method includes obtaining a first frame of image data including a plurality of pixels. Each pixel of the plurality of pixels is associated with a respective color value and a respective depth value. The first frame renders a scene from a first point of view. The method includes generating a three-dimensional (3D) polygon mesh using the plurality of pixels and the respective depth values. The 3D polygon mesh includes a plurality of portions. Each respective portion defines a respective plurality of vertices defining a respective mesh density representative of a density of the respective plurality of vertices. Each vertex is associated with a corresponding pixel of the first frame of image data. The method includes generating a second frame of image data via reprojection using the generated 3D polygon mesh. The second frame of image data has a second point of view different from the first point of view.