Clustering based point cloud segmentation method and system

Abstract

The invention provides a clustering based point cloud segmentation method and system. The method comprises the following steps: calculating the normal vector, plane curvature and compatible set of each point, realized as follows, firstly constructing a k-d tree for the inputted point clouds, and then using the neighbor K points nearest to one point to get the normal vector and plane curvature of the point; clustering the point clouds, constructing a link table and a clustering center table to obtain a set of all clusters; conducting patch processing which includes constructing initial patches, including for each cluster in the set of clusters, and using a plane for approximate fitting to the corresponding point clouds for an MCS fitting plane, the normal vector, plane curvature and compatible set; and conducting patch combination for the final cloud point segmentation result. On the basis of the traditional region growing algorithm, the method and system provided by the invention directly use the normal vector and the plane curvature of the point clouds to carry out rapid classification, and does not need extra calculations to achieve fast segmentation.

Description

technical field [0001] The invention belongs to the technical field of point cloud processing, and in particular relates to a clustering-based point cloud segmentation technical solution. Background technique [0002] With the continuous upgrading of laser scanning technology, 3D laser scanners can quickly and easily obtain high-precision point cloud data of the target object, and the corresponding 3D model can be obtained by using point cloud data modeling. This technology has penetrated into smart cities Construction, machinery manufacturing, reverse engineering and many other industries. Due to the scattered laser point cloud and the similarities and differences in the nature of the surfaces, the unified modeling and processing of massive point cloud data will increase the difficulty of the algorithm and the complexity of mathematical representation. Therefore, the point cloud must first be segmented and classified, and treated separately. [0003] The currently more com...

AI Technical Summary

Problems solved by technology

The problem with this method is that it is difficult to select a suitable seed point and distinguish a smooth boundary, and its regional growth is greatly affected by the set threshold, and it is difficult to select a suitable growth criterion.

The traditional clustering-based method has certain advantages for surface blocks with obvious surface types, but for complex surfaces, it is difficult to directly determine the number of surface classifications and surface types, and it is difficult for fine fragments that are prone to appear. Performing secondary processing also increases the difficulty of the algorithm

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