38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings
Series title:
Lecture Notes in Computer Science
Series volume:
9796
Organizer (entity):
German Association for Pattern Recognition (DAGM)
Conference title:
German Conference on Pattern Recognition (38., 2016, Hannover)
Conference title:
38th German Conference on Pattern Recognition (GCPR 2016)
Venue:
Hannover
Year of conference:
2016
Date of conference beginning:
12.09.2016
Date of conference ending:
15.09.2016
Place of publication:
Cham
Publisher:
Springer International Publishing
Year:
2016
Pages from - to:
131-142
Language:
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... »