TY - GEN
T1 - Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding
AU - Verdoja, Francesco
AU - Thomas, Diego
AU - Sugimoto, Akihiro
N1 - Publisher Copyright:
© 2017 IEEE.
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.
UR - http://www.scopus.com/inward/record.url?scp=85030221172&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030221172&partnerID=8YFLogxK
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
T2 - 2017 IEEE International Conference on Multimedia and Expo, ICME 2017
Y2 - 10 July 2017 through 14 July 2017
ER -