Surface segmentation method based on multi-feature fusion for regionally grown workpiece point clouds

In order to improve the segmentation accuracy of 3D point cloud model in feature ambiguous region, the unsupervised clustering algorithm based on surface fusion features combines the depth residuals with the normal deviation angle to form the fusion features with good descriptive properties and significant differentiation. Adopting the point cloud local density median proximity points as the candidate points for point cloud region growth, using point cloud cluster features instead of individual point features, for the problem of feature ambiguity in the edge region, the unattributed points are reclustered, so as to improve the cluster segmentation in the way of extended growth; experiments are conducted on the selected model dataset, and the results show that the method can achieve the reasonable segmentation of the complex point cloud model, and the under-segmentation situation has a obvious improvement on the under-segmentation situation.