3D Point Cloud Compression Using Overlap-Based Dual Encoding
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Solution Overview
Problem
The large size of dense LiDAR point clouds, ranging from 1 GB to several GBs, poses challenges in storage and transmission due to the substantial space and bandwidth requirements, necessitating efficient compression and decompression methods.
Innovation Solution
A method is employed to divide 3D point data into subsets and apply different compression schemes (CST A and CST B) based on the overlap of point clouds, where CST A converts 3D points to 2D range images and CST B uses octree coding, optimizing the encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense LiDAR point clouds are stored in original format, then complete 3D map accuracy is preserved, but storage space and transmission bandwidth requirements become prohibitively large
Solution Approach 1:
The patent divides the set of 3D points into multiple subsets based on spatial distribution and point cloud overlap characteristics. Different compression schemes are applied to different subsets, allowing the system to preserve accuracy for critical regions while compressing less important areas, thus resolving the contradiction between maintaining 3D map accuracy and reducing storage space requirements.
Solution Approach 2:
The patent applies different compression schemes (CST A for low overlap, CST B for high overlap) to different subsets of point data based on local characteristics. This local quality approach ensures that regions with high point cloud overlap are compressed more aggressively while regions with low overlap maintain higher fidelity, optimizing the balance between storage efficiency and accuracy preservation.
2Reliability
If dense LiDAR point clouds are transmitted in original format, then complete 3D map data integrity is maintained, but transmission bandwidth consumption increases substantially
Solution Approach 1:
The patent segments point cloud data into subsets with different overlap characteristics and applies differential compression. This segmentation allows the system to transmit data with optimized bandwidth usage while maintaining integrity for critical regions, resolving the contradiction between data integrity and bandwidth consumption.
Solution Approach 2:
The patent changes compression parameters based on point cloud overlap characteristics. By dynamically selecting compression schemes (CST A or CST B) based on local overlap metrics, the system adapts transmission quality to local requirements, maintaining data integrity where needed while reducing overall bandwidth consumption.
3Quantity of substance
If different compression schemes are applied to different point subsets, then compression efficiency is optimized, but encoding complexity increases
Solution Approach 1:
The patent segments point clouds into subsets based on overlap characteristics and applies different compression schemes to each subset. This segmentation strategy optimizes compression efficiency by matching compression methods to data characteristics, while the automated segmentation process manages encoding complexity through systematic classification rather than manual intervention.
Solution Approach 2:
The patent automatically adjusts compression parameters based on point cloud overlap metrics. By computing overlap characteristics and selecting appropriate compression schemes (CST A for low overlap, CST B for high overlap) through automated parameter changes, the system achieves optimal compression efficiency while managing encoding complexity through algorithmic decision-making rather than manual configuration.
Data Source
AI summary
A method (900) of encoding point data identifying a set of points in a three-dimensional (3D) space (3D points) is provided. The set of 3D points correspond to a set of physical points of a real-world environment. The method comprises dividing (s902) the set of 3D points into a first subset of 3D points and a second subset of 3D points, encoding (s904) first 3D point data identifying the first subset of 3D points using a first compression scheme, thereby generating first encoded point data, and encoding (s906) second 3D point data identifying the second subset of 3D points using a second compression scheme, thereby generating second encoded point data. The first compression scheme and the second compression scheme are different.