The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Center-based 3D Object Detection and Tracking
Open accessAbstract
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. CenterPoint achieved state-of-the-art performance on the nuScenes benchmark for both 3D detection and tracking, with 65.5 NDS and 63.8 AMOTA for a single model. On the Waymo Open Dataset, Center-Point outperforms all previous single model methods by a large margin and ranks first among all Lidar-only submissions. The code and pretrained models are available at https://github.com/tianweiy/CenterPoint.
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Cites
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Cited by
Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation
Liu, Z., Tang, H., Amini, A., Yang, X., Mao, H., Rus, D. L., & Han, S. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160968 · Cited by 1,307
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., & Tai, C.-L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00116 · Cited by 990
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection
Li, Y., Yu, A. W., Meng, T., Caine, B., Ngiam, J., Peng, D., Shen, J., Lu, Y., Zhou, D., Le, Q. V., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01667 · Cited by 563
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
Chen, Y., Liu, J., Zhang, X., Qi, X., & Jia, J. (2023) · arXiv · doi:10.48550/arxiv.2303.11301 · Cited by 486
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection
Wang, C., Ma, C., Zhu, M., & Yang, X. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01162 · Cited by 474
Metadata
- Authors
- Yin, T., Zhou, X., & Krahenbuhl, P.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 11779-11788
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3167095230
- Cited by
- 2,082
- References
- 66
- FWCI
- 108.81
- Type
- conference paper
- Language
- en
- Open access
- green
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