A Segmentation and Topology Denoising Method for Three-dimensional (3-D) Point Cloud Data Obtained from Laser Scanning
X-B. Fu, G-R. Zhang, T. Kong, Y-C. Zhang, L. Jing and Y-B. Li
A new segmentation and topology denoising method is proposed wherein the laser scanning three-dimensional (3-D) point cloud data and datum points are extracted. The laser scanned 3-D point cloud model is cut in the horizontal plane. The minimum distance between the point cloud data on the horizontal cutting plane and the geodesic line of the datum point is the feature points. The skeleton of the 3-D point cloud model is constituted by the feature points and the point cloud data are segmented according to the skeleton feature points. In the 3-D space of laser scanning point cloud data the original point cloud is transformed in two-dimensional (2-D) space by the spatial topological mapping function. The distance weight is computed by segmented point cloud data. In the process of topological transformation, combining with the distance weight of the sampling point, the topology transformation function is built. The noise point and the non-noise point can be separated by the topology transformation. The large-scale noise points are removed by comparison with a set threshold. The fairing direction of small scale noise is calculated according to the curvature and point cloud density. The small scale noise point is moved along the fairing direction to achieve the effect of denoising of the point cloud data. The method is verified by the point cloud data of laser scanning ring forgings in this paper.
Keywords: Laser scanner, point cloud data, denoising, skeleton extraction, point cloud segmentation, distance weight, topology transformation