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 language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Multimedia and Expo, ICME 2017 |
| Publisher | IEEE Computer Society |
| Pages | 1285-1290 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509060672 |
| DOIs | |
| Publication status | Published - Aug 28 2017 |
| Event | 2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong Duration: Jul 10 2017 → Jul 14 2017 |
Publication series
| Name | Proceedings - IEEE International Conference on Multimedia and Expo |
|---|---|
| ISSN (Print) | 1945-7871 |
| ISSN (Electronic) | 1945-788X |
Other
| Other | 2017 IEEE International Conference on Multimedia and Expo, ICME 2017 |
|---|---|
| Country | Hong Kong |
| City | Hong Kong |
| Period | 7/10/17 → 7/14/17 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications
Cite this
Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding. / Verdoja, Francesco; Thomas, Diego Gabriel Francis; Sugimoto, Akihiro.
2017 IEEE International Conference on Multimedia and Expo, ICME 2017. IEEE Computer Society, 2017. p. 1285-1290 8019382 (Proceedings - IEEE International Conference on Multimedia and Expo).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding
AU - Verdoja, Francesco
AU - Thomas, Diego Gabriel Francis
AU - Sugimoto, Akihiro
PY - 2017/8/28
Y1 - 2017/8/28
N2 - 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.
AB - 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.
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U2 - 10.1109/ICME.2017.8019382
DO - 10.1109/ICME.2017.8019382
M3 - Conference contribution
AN - SCOPUS:85030221172
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
SP - 1285
EP - 1290
BT - 2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PB - IEEE Computer Society
ER -