3D Mesh Model Compression Using Multiple KD-Trees
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Solution Overview
Problem
Existing compression methods for large 3D engineering models are inefficient due to the lack of effective handling of redundancy between connected components and the inability to detect and compress repeating patterns across various scales, leading to suboptimal compression ratios and increased storage needs.
Innovation Solution
The use of multiple KD-trees, each for a cluster of spatially aggregated points, to improve compression efficiency by reducing the depth of the KD-tree and thus the length of the output data stream, while clustering points based on their spatial positions to create more compact clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single KD-tree is used to organize and compress point data sets with spatial aggregation, then the compression method is simple, but the KD-tree becomes unreasonably deep and the output data stream becomes longer than necessary
Solution Approach 1:
The patent divides the set of repeating instances into multiple spatial clusters based on their positions, and creates a separate KD-tree for each cluster. This segmentation of the data into manageable groups allows each KD-tree to be shallower and more efficient, directly resolving the issue of excessively deep trees and long data streams while maintaining compression effectiveness.
2Device complexity
If components are compressed separately in multi-connected 3D models, then the compression method is simple, but the compression performance is relatively ineffective due to redundancy between components
Solution Approach 1:
The patent merges the compression of multiple connected components by identifying and compressing repeating instances across different components simultaneously. By detecting spatial aggregation of repeating instances and treating them as a unified set for compression, the method eliminates redundancy between components and achieves significantly better compression performance.
Solution Approach 2:
The patent uses copying by identifying repeating instances of connected components across different positions and scales, then compressing only the unique geometric data once while referencing the repeated instances through their spatial positions. This copying approach eliminates redundant storage of identical geometric patterns.
3Measurement precision
If PCA of positions of vertices is used, then components with same geometry and different connectivity have the same mean and orientation axes, but this approach is not suitable for detecting and compressing repeating patterns in various scales
Solution Approach 1:
The patent introduces spatial position as a new dimension for analysis, clustering repeating instances based on their 3D coordinates rather than relying solely on PCA of vertex positions. This spatial dimensioning allows the method to detect repeating patterns across different scales and positions, overcoming the limitations of traditional PCA-based approaches.
Data Source
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AI summary
For most large 3D engineering models, the instance positions of repeating instances of connected components show significant multiple spatial aggregation. The invention uses several KD-trees, each for one cluster of points which are spatially aggregated. The multiple KD-trees generate a relatively short data stream, and thus improve the total compression ratio. A method for encoding points of a 3D mesh model comprises steps of determining that the mesh model comprises repeating instances of a connected component, and determining for each repeating instance at least one reference point, clustering the reference points of the repeating instances into one or more clusters, and encoding the clustered reference points using KD-tree coding, wherein for each cluster a separate KD-tree is generated.