Attribute-Based Point Cloud Strip Division
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The large data volume of high-precision point clouds poses challenges for storage and transmission, and existing point cloud strip division technologies are underdeveloped, limiting efficient processing and compression.
Innovation Solution
An attribute-based point cloud strip division method that performs spatial division and sorts attribute values to gather points with similar attributes and spatial positions, allowing for efficient compression and independent coding, enhancing fault tolerance and compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud is divided into strips using traditional uniform or non-uniform geometric division, then the point cloud can be processed in parallel, but the compression performance is limited due to lack of attribute redundancy exploitation
Solution Approach 1:
The point cloud is divided into multiple local point clouds through spatial segmentation using binary tree structure, and then each local point cloud is further divided into strips. This two-level segmentation enables both parallel processing and effective attribute redundancy exploitation within each strip, resolving the contradiction between productivity and compression performance.
Solution Approach 2:
The patent changes the division parameter from pure geometric criteria to attribute-based criteria (color, reflectance, normal vectors). By sorting points according to attribute values and dividing based on attribute thresholds, the method creates strips with homogeneous attributes that maximize compression efficiency while maintaining parallel processing capability.
2Loss of information
If attribute-based sorting and division is performed to improve compression performance, then more computational complexity is required
Solution Approach 1:
The computational task is segmented into two independent stages: first spatial division into local point clouds, then attribute-based strip division within each local region. This segmentation allows parallel processing of different local point clouds, distributing computational complexity across multiple processors while achieving high compression performance within each strip.
Solution Approach 2:
The patent applies different division strategies to different regions: coarse spatial division first, then attribute-based sorting only within each local point cloud. This local quality approach reduces overall computational complexity by limiting the scope of attribute sorting to smaller local regions rather than the entire point cloud, while still achieving effective compression.
3Reliability
If point cloud is divided into independent strips for parallel processing, then system fault tolerance is improved, but coding efficiency may be reduced due to lack of inter-strip optimization
Solution Approach 1:
The point cloud is segmented into independent strips that can be processed and transmitted separately, improving fault tolerance. Within each strip, attribute sorting creates homogeneous regions that maximize compression efficiency. The independent coding of strips maintains fault tolerance while the attribute-based organization within strips ensures high coding efficiency is not compromised.
Data Source
AI summary
An attribute-based point cloud strip division method. The method comprises: first, performing spatial division of a certain depth on a point cloud to obtain a plurality of local point clouds; and then, sorting the attribute values in the local point clouds, and on the basis of such, further performing point cloud strip division so as to obtain point cloud strips that have low geometric overhead and a uniform number of points. By means of comprehensively using the spatial position and attribute information of the point clouds, the points having similar attributes and related spatial positions are gathered as much as possible in one strip during strip division, which is convenient for making full use of the redundancy of the attribute information between adjacent points, and improving the performance of point cloud attribute compression. At the same time, independent coding between strips supports random access, improves coding efficiency, and prevents the accumulation and spread of coding errors, thus enhancing the fault tolerance of the system.