3D Point Cloud Flattening for Curved Surface Defect Measurement
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Solution Overview
Problem
Existing machine vision systems struggle to reliably detect and evaluate defects and features on curved surfaces, such as cylinders and spheres, due to inefficiencies in mesh/surface reconstruction and sensitivity to lighting conditions, particularly when dealing with small or minute features.
Innovation Solution
A system and method that directly processes unorganized 3D point clouds by flattening them along cylindrical or spherical surfaces, allowing for blob detection and feature extraction without requiring mesh or voxel grid representations, and converting back to the original 3D space for accurate measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mesh/surface reconstruction is used to detect blobs on curved surfaces, then detection capability is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent extracts only the essential geometric information needed for blob detection by directly processing point cloud data to identify surface deviations, rather than performing complete mesh reconstruction. This extraction approach maintains detection reliability while significantly reducing processing complexity and time.
Solution Approach 2:
The patent segments the curved surface into local regions and processes each region independently to detect blobs. By dividing the complex reconstruction task into smaller localized operations, the system achieves reliable detection without the computational burden of full surface reconstruction.
2Measurement precision
If mesh/surface reconstruction is performed, then surface representation accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary filtering and organization of point cloud data before blob detection, pre-processing the data to enhance surface representation accuracy. This preliminary action reduces the need for time-consuming post-processing and full mesh reconstruction while maintaining measurement precision.
3Device complexity
If traditional blob analysis tools referencing planar surfaces are used, then processing simplicity is maintained, but detection reliability on curved surfaces deteriorates
Solution Approach 1:
The patent applies local quality analysis by adapting the reference surface to match the local curvature at each detection point. Instead of using a uniform planar reference, the system dynamically adjusts the reference geometry to the local surface characteristics, maintaining processing simplicity while significantly improving detection reliability on curved surfaces.
Data Source
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AI summary
This invention provides a system and method that efficiently detects objects imaged using a 3D camera arrangement by referencing a cylindrical or spherical surface represented by a point cloud, and measures variant features of an extracted object including volume, height, and center of mass, bounding box, and other relevant metrics. The system and method, advantageously, operates directly on unorganized and unordered points, requiring neither a mesh/surface reconstruction nor voxel grid representation of object surfaces in a point cloud. Based upon a cylinder/sphere reference model, an acquired 3D point cloud is flattened. Object (blob) detection is carried out in the flattened 3D space, and objects are converted back to the 3D space to compute the features, which can include regions that differ from the regular shape of the cylinder/sphere. Downstream utilization devices and/or processes, such as part reject mechanism and/or robot manipulators can act on the identified feature data.