3D Point Cloud Ring Detection via Cycle Basis
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Solution Overview
Problem
Detecting a closed ring or annular shape in a 3D point cloud is challenging due to irregular shapes and distortions, making it difficult to identify continuous features and distinguish between real and false gaps, especially in sparse point clouds.
Innovation Solution
A computer-implemented system filters a 3D point cloud by eliminating edges longer than an adaptive threshold, generating a second undirected graph, and detecting a minimum cycle basis to identify a cycle path representing the closed ring, unaffected by distortions and folds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge filtering with adaptive threshold is applied to eliminate long edges, then detection accuracy of closed rings is improved, but computational complexity increases
Solution Approach 1:
The patent applies edge filtering with adaptive threshold as a preliminary step before cycle basis detection. By pre-processing the graph to eliminate edges longer than the adaptive threshold, the system reduces the search space for subsequent cycle detection, thereby improving detection accuracy while managing computational complexity through staged processing
Solution Approach 2:
The patent dynamically adjusts the adaptive threshold parameter based on the specific characteristics of each 3D point cloud dataset. This parameter optimization allows the system to adaptively balance between retaining sufficient edges for accurate ring detection and eliminating enough edges to reduce computational burden, achieving both improved accuracy and controlled complexity
2Measurement precision
If minimum cycle basis detection is performed on filtered graph, then identification of closed ring shapes is improved, but processing time increases
Solution Approach 1:
The patent segments the cycle detection process by first filtering edges to create a simplified graph structure, then performing cycle basis detection on this reduced graph. This segmentation separates the computational burden into two stages: edge filtering (which reduces complexity) and cycle detection (which operates on already-simplified data), thereby improving identification accuracy while reducing overall processing time compared to detecting cycles in the complete unfiltered graph
Solution Approach 2:
By performing edge filtering as a preliminary action before minimum cycle basis detection, the patent prepares an optimized graph structure that retains essential ring-forming edges while removing redundant long edges. This pre-processing step reduces the number of edges the cycle detection algorithm must evaluate, thus improving identification accuracy without proportionally increasing processing time
3Reliability
If adaptive threshold filtering is applied to sparse point clouds, then detection reliability is improved, but edge retention may be reduced
Solution Approach 1:
The patent employs an adaptive threshold that is dynamically calculated based on the density and characteristics of the specific 3D point cloud being analyzed. For sparse point clouds, the adaptive threshold automatically adjusts to be more permissive, allowing more edges to be retained despite the sparsity. This parameter adaptation ensures detection reliability is maintained by preserving sufficient edges to form complete rings, while still filtering out spurious long edges that would reduce reliability
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to detecting a closed ring in a three-dimensional (3D) point cloud via cycle basis. A system can comprise a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components can comprise a filtering component that can filter a first undirected graph of a three-dimensional (3D) point cloud, by eliminating one or more edges of the first undirected graph that are longer than an adaptive threshold, wherein filtering the first undirected graph can produce a second undirected graph; and a detection component that can detect a minimum cycle basis of the second undirected graph to determine a cycle path that can traverse an irregular annular shape that is represented by the 3D point cloud.