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

Francesco Verdoja, Diego Thomas, Akihiro Sugimoto

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

30 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2017 IEEE International Conference on Multimedia and Expo, ICME 2017
出版社IEEE Computer Society
ページ1285-1290
ページ数6
ISBN(電子版)9781509060672
DOI
出版ステータス出版済み - 8 28 2017
イベント2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, 香港
継続期間: 7 10 20177 14 2017

出版物シリーズ

名前Proceedings - IEEE International Conference on Multimedia and Expo
ISSN(印刷版)1945-7871
ISSN(電子版)1945-788X

その他

その他2017 IEEE International Conference on Multimedia and Expo, ICME 2017
国/地域香港
CityHong Kong
Period7/10/177/14/17

All Science Journal Classification (ASJC) codes

  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用

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