Zhen Dong, Bisheng Yang, Pingbo Hua, Sebastian Scherer

Abstract：Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., SEMANTIC3D.NET dataset) and low-quality RGB-D point clouds (i.e., S3DIS dataset) with six metrics of (94.2%, 95.1%, 2.9%, 3.8%, 93.6%, 94.1%) and (90.4%, 91.4%, 8.2%, 7.6%, 90.8%, 91.7%) respectively.

Keywords:Plane segmentation；Multiscale supervoxel；Hybrid region growing；Energy optimization；Simulated annealing；Guided sampling

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