Abstract
A method based on plane segmentation and dimensionality reduction for extracting incomplete and slow contour features of object point clouds is proposed. The method consists of two main steps: plane segmentation and contour extraction. In plane segmentation, the random sample consensus (Random Sample Consensus, RANSAC) algorithm is optimized based on principal component analysis (Principal Component Analysis, PCA); the optimized planar point cloud is then subjected to dimensionality reduction, and the contour features are extracted using gradients. Experimental results show that the method can effectively segment point clouds and extract the contours of target surfaces, and has great potential for application in industrial inspection and other fields.
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