An algorithm for Contiguous PAtch Segmentation (CPAS) in 3D pointclouds is proposed. In contrast to current state-of-the-art algorithms, CPAS is robust, scalable and provides a more complete description by simultaneously detecting contiguous patches as well as delineating object boundaries. Our algorithm uses a voxel grid to divide the scene into non-overlapping voxels within which clipped planes are fitted with RANSAC. Using a Dirichlet process mixture (DPM) model of Gaussians and connected component analysis, voxels are clustered into contiguous regions. Finally, we use importance sampling on the convexhull of each region to obtain the underlying patch and object boundary estimates. For urban scenes, the segmentation represents building walls, ground and roof elements (Fig. 1). We demonstrate the robustness of CPAS using data sets from both image matching and raw LiDAR scans.
CITATION STYLE
Nguatem, W., & Mayer, H. (2016). Contiguous patch segmentation in pointclouds. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9796 LNCS, pp. 131–142). Springer Verlag. https://doi.org/10.1007/978-3-319-45886-1_11
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