3D Volume Compression Using Surface Voxel Extraction
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Solution Overview
Problem
Existing Truncated Signed Distance Function (TSDF) fusion algorithms face challenges in efficiently reducing data volume while maintaining high-quality 3D surface representation, particularly due to the trade-off between high-resolution voxel grids and increased data size, and the complexity of multi-resolution voxel structures.
Innovation Solution
A method involving selecting necessary voxels using a marching cubes algorithm, representing their location information with a tree structure, binarizing and serializing this information, and adaptively quantizing truncated signed distance values based on voxel resolution to compress 3D volume data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution voxel grid is used to maintain high quality of 3D surface, then quality of 3D surface is improved, but amount of data increases greatly
Solution Approach 1:
The patent extracts only the necessary voxels that contribute to forming the geometric surface using the marching cubes algorithm, eliminating unnecessary voxels from the TSDF volume. This extraction approach reduces the quantity of data while preserving the quality of the 3D surface representation by retaining only voxels that are actually needed for accurate surface reconstruction.
Solution Approach 2:
The patent segments the voxel grid into necessary and unnecessary voxels based on their contribution to the geometric surface. By classifying and separating voxels according to their relevance, the method reduces data volume while maintaining surface quality. The segmentation is performed through the marching cubes algorithm that identifies and isolates essential voxels for surface formation.
2Quantity of substance
If multi-resolution TSDF volume is configured by adaptively decreasing resolution in simple regions, then amount of data is reduced, but structure becomes complicated
Solution Approach 1:
Instead of creating a multi-resolution structure with varying voxel densities, the patent extracts only the necessary voxels from a uniform-resolution grid. This approach reduces data量 while maintaining a simple, uniform TSDF volume structure. The marching cubes algorithm identifies and removes unnecessary voxels, preserving the simplicity of the overall data structure.
3Measurement precision
If adjacent voxels are stored in TSDF volume to enable hole-free watertight 3D surface, then quality of 3D surface is improved, but number of unnecessary voxels increases
Solution Approach 1:
The patent uses the marching cubes algorithm to identify and extract only the necessary voxels that contribute to the geometric surface, removing adjacent voxels that do not contribute to surface formation. This extraction maintains the quality of the 3D surface by keeping essential voxels while eliminating unnecessary ones, achieving hole-free watertight surfaces with reduced voxel counts.
Solution Approach 2:
The patent applies partial action by selectively retaining only the necessary voxels rather than storing all adjacent voxels. The marching cubes algorithm determines the minimum set of voxels needed for accurate surface representation, avoiding the excessive storage of all neighboring voxels while still achieving hole-free surfaces.
Data Source
AI summary
Disclosed herein are an apparatus and method for compressing 3D volume data. The apparatus for compressing three-dimensional (3D) volume data includes one or more processor, and execution memory configured to store at least one program that is executed by the one or more processors, wherein the at least one program is configured to receive 3D volume data for data compression, select necessary voxels that contribute to formation of a geometric surface from the 3D volume data using a marching cubes algorithm, represent location information of the selected necessary voxels by a tree structure, and binarize the location information of the necessary voxels by serializing the tree structure, and quantize truncated signed distance values to surfaces adjacent to the necessary voxels constituting a volume from the 3D volume data using the binarized location information of the necessary voxels.