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 | IEEE International Conference on Multimedia and Expo (ICME 2017) |
Place of Publication | Hong Kong |
Pages | 1285-1290 |
Number of pages | 6 |
ISBN (Electronic) | 9781509060672 |
DOIs | |
Publication status | Published - 28 Aug 2017 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE International Conference on Multimedia and Expo - Hong Kong, Hong Kong Duration: 10 Jul 2017 → 14 Jul 2017 |
Conference
Conference | IEEE International Conference on Multimedia and Expo |
---|---|
Abbreviated title | ICME |
Country | Hong Kong |
City | Hong Kong |
Period | 10/07/2017 → 14/07/2017 |
Keywords
- Hierarchical clustering
- Point cloud
- Segmentation
- Supervoxels