In this paper, we report a novel strategy for segmenting 3D point clouds using a voxel structure and graph-based clustering with perceptual grouping laws. It provides a completely automatic solution for partitioning point clouds of man-made infrastructures. Two different segmentation methods using voxel and supervoxel structures are presented and evaluated. To increase the efficiency and the robustness of the segmentation process, the voxelization with octree-based structure is introduced, which can suppress effects of noise, outliers, and unevenly distributed point densities as well. The clustering of over-segmented voxels and supervoxels is achieved via graph theory on the basis of the local contextual information, which is commonly conducted merely with pairwise information in conventional clustering algorithms. The graphical model is constructed according to perceptual grouping laws, considering geometric information associated with points. Experiments using both laser scanning and photogrammetric point clouds have demonstrated that the proposed methods can achieve good results, especially complex scenes and nonplanar object surfaces, with F1-measures better than 0.67 for all the testing samples. Quantitative comparisons between the proposed approaches and other representative segmentation methods also confirm the effectiveness and the efficiency of the former. Moreover, a series of experiments is carried out, to investigate the methods’ sensitivity with respect to various parameters on the segmentation results.     «

In this paper, we report a novel strategy for segmenting 3D point clouds using a voxel structure and graph-based clustering with perceptual grouping laws. It provides a completely automatic solution for partitioning point clouds of man-made infrastructures. Two different segmentation methods using voxel and supervoxel structures are presented and evaluated. To increase the efficiency and the robustness of the segmentation process, the voxelization with octree-based structure is introduced, which...     »