38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings
Reihentitel:
Lecture Notes in Computer Science
Bandnummer Reihe:
9796
Veranstalter (Körperschaft):
German Association for Pattern Recognition (DAGM)
Konferenztitel:
German Conference on Pattern Recognition (38., 2016, Hannover)
Konferenztitel:
38th German Conference on Pattern Recognition (GCPR 2016)
Tagungsort:
Hannover
Jahr der Konferenz:
2016
Datum Beginn der Konferenz:
12.09.2016
Datum Ende der Konferenz:
15.09.2016
Verlagsort:
Cham
Verlag:
Springer International Publishing
Jahr:
2016
Seiten von - bis:
131-142
Sprache:
Englisch
Abstract:
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 convex-hull 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. «
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 com... »