A Fast Point Cloud Segmentation Algorithm Based on Region Growth

EasyChair Preprint 1048

Abstract

Point cloud segmentation is a key prerequisite for object classification recognition. We propose a fast region growing algorithm by using the neighborhood search, filter sampling, Euclidean clustering and region growth. Segmentation experiment on point cloud data in indoor environment demonstrated that segmentation accuracy and efficiency were improved by the proposed algorithm.

Keyphrases: Euclidean clustering, point cloud segmentation, regional growth

@booklet{EasyChair:1048,
  author    = {Xiaofeng Ma and Wei Luo and Mingquan Chen and Jiahui Li and Xin Yan and Xia Zhang and Wei Wei},
  title     = {A Fast Point Cloud Segmentation Algorithm Based on Region Growth},
  howpublished = {EasyChair Preprint 1048},
  year      = {EasyChair, 2019}}