Point Cloud Noise Filtering via Topological Structure Analysis
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Solution Overview
Problem
Manufacturing enterprises face challenges in accurately and efficiently verifying component correctness due to the presence of noise points in point clouds, which affect processing speed and accuracy.
Innovation Solution
A system and method for filtering point clouds, involving an application server with modules for acquiring, establishing topological structure, selecting points, searching near points, determining noise points, and filtering or smoothing them, to automatically and accurately remove noise from the point cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual verification tasks are used, then flexibility in verification processes is maintained, but accuracy and consistency of verification deteriorate
Solution Approach 1:
The system enables automatic self-verification by having the computer equipment autonomously scan objects, process point cloud data, compare measurement data with design data, and generate verification results without human intervention, thereby improving accuracy and consistency while increasing automation
2Measurement precision
If noise points are present in the point cloud, then the point cloud contains complete raw data, but processing speed and accuracy deteriorate
Solution Approach 1:
The system extracts and removes noise points from the point cloud by comparing each point's coordinates with its neighboring points, identifying points that deviate significantly from their local environment, and eliminating them to improve processing accuracy and speed
3Productivity
If traditional point cloud processing methods are used, then implementation simplicity is maintained, but processing speed and accuracy deteriorate due to noise points
Solution Approach 1:
The system segments the point cloud processing into distinct stages: establishing topological structures to organize spatial relationships, selecting sample points for noise detection, comparing coordinates to identify noise, and removing noise points, thereby improving processing efficiency through structured methodology
Solution Approach 2:
The system introduces a topological structure as an intermediary framework that organizes points in the point cloud before processing, creating spatial relationships and neighborhoods that enable efficient noise detection and removal while maintaining processing speed
Data Source
AI summary
A method for filtering a point cloud is provided. The method includes: (a) acquiring a point cloud of an object from a point cloud obtaining device; (b) establishing a topological structure for the point cloud; (c) selecting a maiden point from the point cloud as a selected point; (d) searching a plurality of points which are near to the selected point from the point cloud according to the topological structure as near points of the selected point; (e) determining whether the selected point is a noise point by comparing coordinate values of the selected point and coordinate values of the near points; (f) deleting or smoothing the noise point from the point cloud; and repeating steps from (c) to (f), until all points in the point cloud have been selected. A related system is also provided.