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arXiv

Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)

This is the official implementation of Focals Conv (CVPR 2022), a new sparse convolution design for 3D object detection (feasible for both lidar-only and multi-modal settings). For more details, please refer to:

Focal Sparse Convolutional Networks for 3D Object Detection [Paper] [Github]
Yukang Chen, Yanwei Li, Xiangyu Zhang, Jian Sun, Jiaya Jia

KITTI dataset

Car@R11 Car@R40 download
PV-RCNN + Focals Conv 83.91 85.20 Google | Baidu (key: m15b)
PV-RCNN + Focals Conv (multimodal) 84.58 85.34 Google | Baidu (key: ie6n)
Voxel R-CNN (Car) + Focals Conv (multimodal) 85.68 86.00 Google | Baidu (key: tnw9)

nuScenes dataset

mAP NDS download
CenterPoint + Focals Conv (multi-modal) 63.86 69.41 Google | Baidu (key: 01jh)
CenterPoint + Focals Conv (multi-modal) - 1/4 data 62.15 67.45 Google | Baidu (key: 6qsc)

Citation

If you find this project useful in your research, please consider citing:

@inproceedings{focalsconv-chen,
  title={Focal Sparse Convolutional Networks for 3D Object Detection},
  author={Chen, Yukang and Li, Yanwei and Zhang, Xiangyu and Sun, Jian and Jia, Jiaya},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

License

This project is released under the Apache 2.0 license.

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