Dynamic Mesh Zerotree Coding for Progressive Adaptive Reconstruction
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Solution Overview
Problem
Existing dynamic mesh compression techniques fail to efficiently address scalability and adaptive reconstruction, particularly in terms of resolution and quality, due to the lack of utilization of wavelet coefficient hierarchies and uniform mesh subdivision, leading to excessive processing and data inefficiencies.
Innovation Solution
Applying zerotree coding to subdivision wavelets for dynamic meshes, which organizes wavelet coefficients based on their importance, allowing for adaptive refinement and efficient bitstream truncation, enabling resolution and quality scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If uniform mesh subdivision is applied to achieve resolution scalability, then all regions are refined equally, but this leads to excessive processing and data requirements in regions that do not need high detail
Solution Approach 1:
The patent applies local quality by using zero-tree coding to identify and refine only those mesh regions that require higher detail. Instead of uniform subdivision, the encoder analyzes wavelet coefficients and generates zero-tree structures that mark insignificant regions, allowing the decoder to apply refinement selectively to important areas while maintaining overall resolution scalability.
2Manufacturing precision
If comprehensive mesh data is transmitted to ensure quality, then all wavelet coefficients are encoded, but this increases data requirements and transmission overhead
Solution Approach 1:
The patent extracts and eliminates redundant information by using zero-tree coding to identify wavelet coefficients that are insignificant or correlated with parent coefficients. These redundant coefficients are not encoded, reducing data volume while preserving essential mesh quality information that can be reconstructed at the decoder.
Solution Approach 2:
The patent applies partial action by encoding only the necessary portion of wavelet coefficients required to achieve acceptable mesh quality. The zero-tree structure allows the encoder to selectively transmit coefficient information based on importance, avoiding the transmission of excessive data while maintaining manufacturing precision.
3Measurement precision
If all wavelet coefficients are decoded to maintain reconstruction accuracy, then complete mesh detail is achieved, but this increases processing complexity and computational load
Solution Approach 1:
The patent applies preliminary action by organizing wavelet coefficients into a zero-tree hierarchy during encoding, before decoding. This pre-organization identifies which coefficients are significant and which can be inferred or skipped, allowing the decoder to reconstruct the mesh with reduced processing complexity while maintaining reconstruction accuracy through selective coefficient application.
Data Source
AI summary
Apparatuses and methods are disclosed for progressively encoding a mesh. Techniques disclosed include obtaining displacement data representative of a spatial difference between a base mesh and the mesh to be encoded, transforming the displacement data into wavelet coefficients, computed at multiple resolution levels, and then zerotree encoding the wavelet coefficients in a traversal order according to a zerotree hierarchy, generating a zerotree bitstream of coded displacement data. Further apparatuses and methods are disclosed for progressively decoding the mesh. Techniques disclosed include obtaining the zerotree bitstream that codes the displacement data, zerotree decoding the wavelet coefficients from the bitstream, and inverse transforming the decoded wavelet coefficients into decoded displacement data.