Authors:
Martin Madaras
1
;
2
;
Martin Stuchlík
2
and
Matúš Talčík
3
;
2
Affiliations:
1
Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava, Slovakia
;
2
Skeletex Research, Slovakia
;
3
Masaryk University Brno, Czech Republic
Keyword(s):
Point Cloud, Segmentation, Parallel, Pyramid, GPU, CUDA.
Abstract:
An intelligent automatic robotic system needs to understand the world as fast as possible. A common way to capture the world is to use a depth camera. The depth camera produces an organized point cloud that later needs to be processed to understand the scene. Usually, segmentation is one of the first preprocessing steps for the data processing pipeline. Our proposed pyramid segmentation is a simple, fast and lightweight split- and-merge method designed for depth cameras. The algorithm consists of two steps, edge detection and a hierarchical method for bridgeless labeling of connected components. The pyramid segmentation generates the seeds hierarchically, in a top-down manner, from the largest regions to the smallest ones. The neighboring areas around the seeds are filled in a parallel manner, by rendering axis-aligned line primitives, which makes the performance of the method fast. The hierarchical approach of labeling enables to connect neighboring segments without unnecessary brid
ges in a parallel way that can be efficiently implemented using CUDA.
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