An Automatic Method for Point Cloud Multi-Geometry Feature Recognition

doi:10.13474/j.cnki.11-2246.2017.0038

Abstract

Abstract:

A segmentation method is proposed in this paper,based on local sample for point cloud with rich geometry features(plane,cylinder,sphere). The proposed method firstly divides point cloud by 3D grid. Then a subcell is determined by a random sample point, in which multi-primitive models are fitted, and the candidate models are got by its local score. Then the global scores are determined by statistic inference. Hence, the best model and its consensus set can be selected, and a segmentation can be made on point cloud. The experimental results show that our method can efficiently segment man-made structures rich in regular geometry shape.

Key words: terrestrial laser 3D scanning, segmentation, local sample, statistics inference, normal vector constrain

CLC Number:

P225

SHI Hongbin, YIN Yicheng, YUAN Manfei. An Automatic Method for Point Cloud Multi-Geometry Feature Recognition[J]. 测绘通报, 2017, 0(2): 6-9,53.

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