The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
On the segmentation of 3D LIDAR point clouds
Abstract
This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e.g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e.g. Velodyne scans) is addressed using ground models of non-constant resolution either providing a continuous probabilistic surface or a terrain mesh built from the structure of a range image, both representations providing close to real-time performance. All the algorithms are tested on several hand labeled data sets using two novel metrics for segmentation evaluation.
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Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910
Cited by
Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8462926 · Cited by 967
Octree-based region growing for point cloud segmentation
Vo, A.-V., Truong-Hong, L., Laefer, D. F., & Bertolotto, M. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.01.011 · Cited by 657
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Li, B., Zhang, T., & Xia, T. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.042 · Cited by 577
SegMatch: Segment based place recognition in 3D point clouds
Dube, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., & Cadena, C. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989618 · Cited by 339
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
El Madawi, K., Rashed, H., El Sallab, A., Nasr, O., Kamel, H., & Yogamani, S. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917447 · Cited by 116
Metadata
- Authors
- Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., & Frenkel, A.
- Venue
- 2011 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 2798-2805
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2132360065
- Cited by
- 477
- References
- 2
- FWCI
- 47.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., & Frenkel, A. (2011). On the segmentation of 3D LIDAR point clouds. 2011 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2011.5979818
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