Centroid-Normal Mesh Prediction for Low-Bandwidth XR Geometry Coding
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Solution Overview
Problem
Existing mesh coding methods for 4D content in extended reality (XR) applications do not effectively utilize the intrinsic smoothness of mesh connectivity, leading to inefficient transmission and computational demands due to reliance on vertex data structure signaling, which limits predictive coding effectiveness.
Innovation Solution
Implementing a predictive coding framework using a convolutional neural network for centroid-normal representations to capture cross-scale dependencies in mesh geometry, enabling efficient encoding and decoding of 4D content by generating low-resolution meshes and residual bitstreams for high-resolution reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vertex data structure signaling is used for mesh coding, then mesh connectivity can be represented, but transmission cost increases and predictive coding effectiveness decreases
Solution Approach 1:
The patent extracts connectivity information from the vertex data structure and represents it separately using a connectivity signal. This separation allows the vertex positions to be coded independently while connectivity is encoded through differential values, reducing overall transmission requirements while maintaining complete mesh representation capability.
Solution Approach 2:
The patent transforms connectivity representation from absolute vertex indices to differential connectivity values. By encoding changes in connectivity rather than absolute positions, the system reduces the number of bits required to represent mesh topology, directly addressing the transmission volume issue while preserving connectivity information.
2Quantity of substance
If traditional mesh coding methods are used, then mesh data can be transmitted, but computational intensity increases and visual fidelity decreases
Solution Approach 1:
The patent applies predictive coding by estimating future mesh states based on previous frames. This preliminary action of prediction allows the system to encode only residuals (differences between predicted and actual states), reducing transmission volume while maintaining visual fidelity through accurate reconstruction using the predicted values plus residuals.
Solution Approach 2:
The patent introduces prediction models as intermediary components between the source and destination meshes. These models generate predicted vertex positions and connectivity that serve as a baseline, allowing efficient encoding of deviations while preserving fine geometric details that contribute to visual fidelity.
3Manufacturing precision
If high-resolution mesh data is stored and transmitted, then visual fidelity is maintained, but storage and bandwidth requirements increase significantly
Solution Approach 1:
The patent segments mesh data into multiple independent components: vertex positions, connectivity information, and texture coordinates. Each component is encoded separately with optimized techniques tailored to its specific characteristics, allowing efficient compression while preserving the complete high-resolution mesh representation when reconstructed.
Solution Approach 2:
The patent transitions from encoding complete high-resolution mesh data in spatial dimensions to encoding temporal predictions and spatial residuals. By adding the time dimension through predictive coding, the system reduces the data volume requiring transmission while maintaining visual fidelity through the reconstruction process that combines historical prediction data with current frame residuals.
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.