3D Mesh Compression via Repeating Pattern Clustering
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Solution Overview
Problem
Existing 3D mesh compression algorithms are inadequate for large engineering models with many connected components and non-smooth surfaces, as they fail to efficiently discover and encode repeating geometric feature patterns across various positions, scales, and orientations, leading to inefficient storage and transmission.
Innovation Solution
The method involves normalizing connected components to discover and cluster equivalent geometry patterns, representing each component with an identifier and transformation information, and using a kd-tree and k-means clustering to compress and reconstruct the model, incorporating techniques like quadric error matrix for precise orientation and scale normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional 3D mesh compression algorithms are used, then smooth surfaces with dense meshes can be compressed efficiently, but large engineering models with non-smooth surfaces and arbitrary connectivity cannot be compressed effectively
Solution Approach 1:
The patent segments the 3D mesh model into multiple connected components and further divides each component into geometry feature patterns. This segmentation allows the compression algorithm to handle complex engineering models by processing them as collections of simpler, reusable patterns rather than as monolithic structures, thereby improving both compression efficiency and adaptability to diverse model types.
Solution Approach 2:
The patent identifies repeating geometry feature patterns across different connected components and creates compressed representations that reference these patterns. Instead of storing duplicate geometric data for each instance, the system stores a master pattern definition and references it multiple times with transformation parameters, significantly reducing data redundancy for models with repeated features.
2Device complexity
If each connected component is encoded separately, then the encoding process is simple, but the compression ratio is poor due to redundancy in repeating patterns
Solution Approach 1:
The patent performs preliminary analysis to identify repeating geometry feature patterns across connected components before the actual encoding process. By pre-processing the model to discover pattern repetitions and organizing them into a pattern database, the system enables more efficient compression during encoding without excessively increasing overall complexity, as the pattern matching work is done in advance.
3Quantity of substance
If geometry feature patterns are discovered and clustered, then data redundancy is removed and storage is compacted, but the encoding process becomes more complex
Solution Approach 1:
The patent implements self-service mechanisms where the encoding system automatically discovers repeating geometry patterns, clusters them by similarity, and generates compression representations without requiring manual intervention. The system autonomously performs pattern recognition, clustering, and reference generation, reducing the need for complex external processing while achieving significant data size reduction through automatic redundancy removal.
Data Source
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AI summary
3D models of the engineering class usually have a large number of connected components, with small numbers of large triangles,often with arbitrary connectivity. To enable compact storage and fast transmission of large 3D mesh models, an efficient compression strategy specially designed for 3D mesh models is provide. A method for encoding a 3D mesh model comprises determining and clustering repeating components, normalizing the components, wherein scaling factors are clustered and orientation axes are clustered, encoding the connected components using references to the clusters, and entropy encoding the connected components.