Wavelet-Based Progressive Recompression for Large-Deformed Mesh Data
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Solution Overview
Problem
Current 3D processing technologies face significant challenges in efficiently recompressing deformed mesh data, particularly for large meshes, as existing methods require full decoding and re-compression from scratch, leading to lengthy processing times.
Innovation Solution
A wavelet-based progressive compression method that uses a propagation algorithm to update mesh regions affected by deformation, allowing for fast recompression by extracting and modifying relevant vertices and wavelet coefficients using a filterbank, while preserving connectivity and enabling efficient recompression of deformed meshes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full decoding and re-compression from scratch is performed on deformed mesh data, then compression quality is maintained, but recompression time becomes excessively long
Solution Approach 1:
The mesh data is segmented into multiple resolution levels using hierarchical representation. Only the affected regions at specific resolution levels are identified and processed during recompression, rather than processing the entire mesh from scratch. This segmentation allows selective updating of deformed regions while preserving unchanged regions, significantly reducing recompression time while maintaining compression quality.
Solution Approach 2:
The mesh data is pre-processed into a hierarchical multi-resolution format with pre-computed wavelet coefficients and filterbank structures. This preliminary organization enables rapid identification of affected regions and facilitates incremental updates during recompression, avoiding the need for complete re-compression while preserving compression quality.
2Quantity of substance
If traditional compression methods are used on large meshes with many vertices, then comprehensive compression is achieved, but processing complexity and time increase significantly
Solution Approach 1:
The large mesh data is divided into hierarchical resolution levels, where each level represents a coarser approximation. This segmentation reduces the computational complexity at each processing stage, as operations are performed on progressively smaller data sets from fine to coarse levels, making handling of large meshes feasible without overwhelming processing requirements.
Solution Approach 2:
The mesh processing is extended from a single-resolution domain to a multi-resolution dimensional space. By operating across multiple resolution levels rather than a single level, the system can process large meshes more efficiently, trading computational complexity for the additional dimensional aspect of resolution hierarchy.
3Productivity
If wavelet-based progressive compression is applied to deformed meshes, then recompression speed is improved, but maintaining connectivity and deformation accuracy becomes challenging
Solution Approach 1:
The filterbank and wavelet basis functions are pre-computed and stored in the compressed data structure. During recompression, these pre-computed elements are reused to rapidly transform deformed vertex positions back into wavelet coefficients, maintaining both speed and accuracy without requiring full re-computation of the wavelet basis.
Solution Approach 2:
The unchanged portions of the original compressed data (filterbank, non-affected wavelet coefficients) are copied and reused in the recompressed output. This copying approach preserves the high compression speed while maintaining deformation accuracy in the affected regions, as the reused data ensures consistency with the original mesh structure where no deformation occurred.
Data Source
AI summary
Disclosed are a wavelet-based progressive fast recompression method and system for large-deformed mesh data. Given mesh data is compressed. The compressed mesh data is recompressed by propagating part of a deformed mesh to all meshes based on information extracted from the compressed data when the mesh data is deformed.