Recognising geometric primitives in 3D point clouds of mechanical CAD objects
This addresses the problem of accurately segmenting CAD point clouds for applications in reverse engineering or quality control, though it appears incremental as it builds on Hough transform techniques.
The paper tackles the recognition of geometric primitives in noisy 3D point clouds from mechanical CAD scans, proposing a Hough transform-based method that is robust to noise and outliers and reduces oversegmentation, with results showing competitiveness against existing solutions.
The problem faced in this paper concerns the recognition of simple and complex geometric primitives in point clouds resulting from scans of mechanical CAD objects. A large number of points, the presence of noise, outliers, missing or redundant parts and uneven distribution are the main problems to be addressed to meet this need. In this article we propose a solution, based on the Hough transform, that can recognize simple and complex geometric primitives and is robust to noise, outliers, and missing parts. Additionally, we can extract a series of geometric descriptors that uniquely characterize a primitive and, based on them, aggregate the output into maximal or compound primitives, thus reducing oversegmentation. The results presented in the paper demonstrate the robustness of the method and its competitiveness with respect to other solutions proposed in the literature.