3D Vertex Grouping for Geometry Data Compression
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Solution Overview
Problem
Existing methods for compressing three-dimensional (3D) object data focus primarily on connection information rather than geometry information, leading to inefficient compression due to the larger data size of position information.
Innovation Solution
A data processing apparatus and method that groups vertices into groups, determines a prediction mode for each group based on curvature, and codes the prediction error vector and ID index using an arithmetic coder and bit-plane coder, effectively compressing vertex position information while minimizing prediction error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods focus on compressing connection information rather than position information, then connection information compression is improved, but overall compression efficiency deteriorates because position information occupies greater data volume
Solution Approach 1:
The patent segments vertices into multiple groups based on their spatial distribution and geometric characteristics. Each group is then processed independently with group-specific prediction modes, allowing tailored compression strategies for different geometric regions while maintaining overall compression efficiency.
Solution Approach 2:
The patent applies different prediction modes to different vertex groups based on their local geometric properties such as curvature and surface characteristics. This local quality approach ensures that each group is compressed using the most appropriate method for its specific geometry, optimizing overall compression while preserving accuracy.
2Measurement precision
If vertex position is predicted using only vertices processed during compression or decompression, then processing simplicity is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing vertices to calculate geometric properties such as curvature and normal vectors before the main compression process. These pre-calculated properties are stored and used during prediction to improve accuracy without adding complexity to the core compression algorithm.
Solution Approach 2:
The patent introduces intermediary elements such as prediction mode determination units and geometric property calculators that mediate between the raw vertex data and the compression process. These intermediaries enhance prediction accuracy by providing additional geometric context while maintaining a structured and manageable system architecture.
3Productivity
If vertices are grouped into groups with maximum number limited by threshold, then processing efficiency is improved, but group management complexity increases
Solution Approach 1:
The patent implements dynamic vertex grouping where groups are formed and managed based on real-time geometric criteria and processing requirements. The grouping structure can adapt during processing, allowing efficient management of vertex groups while maintaining flexibility to handle different geometric configurations and processing stages.
Data Source
AI summary
A data processing apparatus and method. A vertex grouping unit of the data processing apparatus may group, into at least one group, a plurality of vertices included in a three-dimensional (3D) object desired to be compressed. A prediction mode determination unit may determine a prediction mode for compressing a vertex position with respect to each of the at least one group. A coder may code a prediction error vector and an identification (ID) index of the prediction mode determined with respect to each of the at least one group.