Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding

Francesco Verdoja, Diego Thomas, Akihiro Sugimoto

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

26 Citations (Scopus)

Abstract

Segmentation of 3D colored point clouds is a research field with renewed interest thanks to recent availability of inexpensive consumer RGB-D cameras and its importance as an unavoidable low-level step in many robotic applications. However, 3D data's nature makes the task challenging and, thus, many different techniques are being proposed, all of which require expensive computational costs. This paper presents a novel fast method for 3D colored point cloud segmentation. It starts with supervoxel partitioning of the cloud, i.e., an oversegmentation of the points in the cloud. Then it leverages on a novel metric exploiting both geometry and color to iteratively merge the supervoxels to obtain a 3D segmentation where the hierarchical structure of partitions is maintained. The algorithm also presents computational complexity linear to the size of the input. Experimental results over two publicly available datasets demonstrate that our proposed method outperforms state-of-the-art techniques.

Original languageEnglish
Title of host publicationIEEE International Conference on Multimedia and Expo (ICME 2017)
Place of PublicationHong Kong
Pages1285-1290
Number of pages6
ISBN (Electronic)9781509060672
DOIs
Publication statusPublished - 28 Aug 2017
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Multimedia and Expo - Hong Kong, Hong Kong
Duration: 10 Jul 201714 Jul 2017

Conference

ConferenceIEEE International Conference on Multimedia and Expo
Abbreviated titleICME
CountryHong Kong
CityHong Kong
Period10/07/201714/07/2017

Keywords

  • Hierarchical clustering
  • Point cloud
  • Segmentation
  • Supervoxels

Fingerprint Dive into the research topics of 'Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding'. Together they form a unique fingerprint.

Cite this