The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Focal Sparse Convolutional Networks for 3D Object Detection
Open accessAbstract
Non-uniformed 3D sparse data, e.g., point clouds or voxels in different spatial positions, make contribution to the task of 3D object detection in different ways. Existing basic components in sparse convolutional networks (Sparse CNNs) process all sparse data, regardless of regular or submanifold sparse convolution. In this paper, we introduce two new modules to enhance the capability of Sparse CNNs, both are based on making feature sparsity learnable with position-wise importance prediction. They are focal sparse convolution (Focals Conv) and its multi-modal variant of focal sparse convolution with fusion, or Focals Conv-F for short. The new modules can readily substitute their plain counterparts in existing Sparse CNNs and be jointly trained in an end-to-end fashion. For the first time, we show that spatially learnable sparsity in sparse convolution is essential for sophisticated 3D object detection. Extensive experiments on the KITTI, nuScenes and Waymo benchmarks validate the effectiveness of our approach. Without bells and whistles, our results outperform all existing single-model entries on the nuScenes test benchmark. Code and models are at github.com/dvlab-research/FocalsConv.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Self-citationFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Self-citationBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
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
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556
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
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513
Self-citationCascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370
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
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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Cited by
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
Self-citationMulti-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
Self-citationCross Modal Transformer: Towards Fast and Robust 3D Object Detection
Yan, J., Liu, Y., Sun, J., Jia, F., Li, S., Wang, T., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01675 · Cited by 182
Self-citationIS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
Yin, J., Shen, J., Chen, R., Li, W., Yang, R., Frossard, P., & Wang, W. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01412 · Cited by 133
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
Chen, Y., Yu, Z., Chen, Y., Lan, S., Anandkumar, A., Jia, J., & Alvarez, J. M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00771 · Cited by 119
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Metadata
- Authors
- Chen, Y., Li, Y., Zhang, X., Sun, J., & Jia, J.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 5418-5427
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4313149358
- Cited by
- 325
- References
- 62
- FWCI
- 27.62
- Type
- conference paper
- Language
- en
- Open access
- green
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