The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
3D Point Cloud Processing and Learning for Autonomous Driving: Impacting Map Creation, Localization, and Perception
Abstract
We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles (AVs), lidar sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an AV. Although much attention has been paid to data collected from cameras, such as images and videos, an increasing number of researchers have recognized the importance and significance of lidar in autonomous driving and have proposed processing and learning algorithms that exploit 3D point clouds. We review the recent progress in this research area and summarize what has been tried and what is needed for practical and safe AVs. We also offer perspectives on open issues that are needed to be solved in the future.
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(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
A method for registration of 3-D shapes
Besl, P. J., & McKay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 18,103
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R. Q., Su, H., Kaichun, M., & Guibas, L. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.16 · Cited by 9,237
(2007) · doi:10.5555/1283383.1283494 · Cited by 7,364
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Self-citationSpatio-Temporal Branching for Motion Prediction using Motion Increments
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Metadata
- Authors
- Chen, S., Liu, B., Feng, C., Vallespi-Gonzalez, C., & Wellington, C.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2021-01
- Volume
- 38
- Issue
- 1
- Pages
- 68-86
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W3116564952
- Cited by
- 258
- References
- 37
- FWCI
- 9.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, S., Liu, B., Feng, C., Vallespi-Gonzalez, C., & Wellington, C. (2021). 3D Point Cloud Processing and Learning for Autonomous Driving: Impacting Map Creation, Localization, and Perception. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2020.2984780
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