The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection
Abstract
Camera and LiDAR are two complementary sensors for 3D object detection in the autonomous driving context. Camera provides rich texture and color cues while LiDAR specializes in relative distance sensing. The challenge of 3D object detection lies in effectively fusing 2D camera images with 3D LiDAR points. In this paper, we present a novel cross-modal 3D object detection algorithm, named PointAugmenting. On one hand, PointAugmenting decorates point clouds with corresponding point-wise CNN features extracted by pretrained 2D detection models, and then performs 3D object detection over the decorated point clouds. In comparison with highly abstract semantic segmentation scores to decorate point clouds, CNN features from detection networks adapt to object appearance variations, achieving significant improvement. On the other hand, PointAugmenting benefits from a novel cross-modal data augmentation algorithm, which consistently pastes virtual objects into images and point clouds during network training. Extensive experiments on the large-scale nuScenes and Waymo datasets demonstrate the effectiveness and efficiency of our PointAugmenting. Notably, PointAugmenting outperforms the LiDAR-only baseline detector by +6.5% mAP and achieves the new state-of-the-art results on the nuScenes leaderboard to date.
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Cites
(2014) · Cited by 33,419
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522
The Cityscapes Dataset for Semantic Urban Scene Understanding
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YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
Self-citationPointNet: 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
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00612 · Cited by 5,400
Deep High-Resolution Representation Learning for Visual Recognition
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2983686 · Cited by 4,894
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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PointPillars: Fast Encoders for Object Detection From Point Clouds
Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., & Beijbom, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01298 · Cited by 4,673
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
Self-citationTransFusion: 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
Focal Sparse Convolutional Networks for 3D Object Detection
Chen, Y., Li, Y., Zhang, X., Sun, J., & Jia, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00535 · Cited by 325
Multi-Modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy
Wang, L., Zhang, X., Song, Z., Bi, J., Zhang, G., Wei, H., Tang, L., Yang, L., Li, J., Jia, C., et al. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3264658 · Cited by 269
Metadata
- Authors
- Wang, C., Ma, C., Zhu, M., & Yang, X.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 11789-11798
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3170030651
- Cited by
- 474
- References
- 34
- FWCI
- 25.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, C., Ma, C., Zhu, M., & Yang, X. (2021). PointAugmenting: Cross-Modal Augmentation for 3D Object Detection. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.01162
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