Centroid-Normal Mesh Prediction for Low-Bandwidth 4D XR Coding
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Solution Overview
Problem
Existing mesh coding techniques for 4D content in extended reality (XR) applications do not effectively leverage the intrinsic smoothness of mesh connectivity, leading to inefficient transmission and computational demands due to signaling vertex data structures, which limits predictive coding effectiveness.
Innovation Solution
A predictive coding framework using convolutional neural networks for centroid-normal representations to capture cross-scale dependencies, enabling efficient encoding and decoding of mesh geometry by predicting centroid and normal vectors, reducing data volume through residual coding and lossless compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vertices are coded using point cloud compression techniques with connectivity information signaled by reference to vertex data structure, then mesh representation can be achieved, but transmission becomes expensive and predictive coding effectiveness is limited
Solution Approach 1:
The patent transforms the mesh representation from vertex-based connectivity references to centroid-normal representations. This parameter change converts the mesh data into a format where each element is represented by its centroid position and normal vector, eliminating the need for connectivity references and enabling efficient predictive coding by capturing intrinsic smoothness of the mesh signal.
Solution Approach 2:
The patent replaces the traditional mechanical reference-based signaling system with a neural network-based predictive coding system. A convolutional neural network is used to predict centroid-normal representations from downsampled versions, substituting the complex reference lookup mechanism with a learned prediction model that leverages cross-scale dependencies.
2Manufacturing precision
If high-resolution mesh data is stored and transmitted to maintain visual fidelity, then quality is improved, but storage and bandwidth requirements become excessive
Solution Approach 1:
The patent segments the mesh representation into multiple resolution levels. A downsampled low-resolution mesh is transmitted along with residual information that can be used to reconstruct the high-resolution mesh. This segmentation allows the system to transmit only the essential information at lower resolution while maintaining the ability to reconstruct high-fidelity content when needed.
Solution Approach 2:
The patent implements a nested representation where the low-resolution mesh serves as a base layer and residual information is added as an outer layer. The centroid occupancy prediction model and normal vector prediction model work together to nest the residual data around the base mesh, allowing efficient storage and transmission while maintaining visual fidelity through hierarchical decomposition.
Data Source
AI summary
Systems and methods are provided for predictive mesh coding based on a centroid-normal (C-N) representation. An encoder generates C-N representations of a high-resolution (hi-res) mesh and a downscaling of the mesh (lo-res mesh), each representation having respective centroids and normals. The encoder generates predicted centroids corresponding to the hi-res mesh based on the lo-res centroids using a centroid prediction model. The encoder generates predicted normals corresponding to the hi-res mesh based on the predicted centroids and lo-res normals using a normal vector prediction model. Residuals are computed for the respective predicted geometry data. The encoder transmits encodings of the lo-res mesh and the residuals for decoding at a client device.