3D point cloud segmentation using topological persistence

William J. Beksi; Nikolaos Papanikolopoulos

ICRA 201614 citations

3D point cloud segmentation using topological persistence

William J. Beksi, Nikolaos Papanikolopoulos

Abstract

In this paper, we present an approach to segment 3D point cloud data using ideas from persistent homology theory. The proposed algorithms first generate a simplicial complex representation of the point cloud dataset. Next, we compute the zeroth homology group of the complex which corresponds to the number of connected components. Finally, we extract the clusters of each connected component in the dataset. We show that this technique has several advantages over state of the art methods such as the ability to provide a stable segmentation of point cloud data under noisy or poor sampling conditions and its independence of a fixed distance metric.

BibTeX

@inproceedings{icra2016_3dpointcloudsegm,
  title = {3D point cloud segmentation using topological persistence},
  author = {William J. Beksi and Nikolaos Papanikolopoulos},
  booktitle = {ICRA 2016},
  year = {2016}
}