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https://github.com/dvlab-research/LargeKernel3D
LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs (CVPR 2023)
https://github.com/dvlab-research/LargeKernel3D
3d nuscenes object-detection scannet semantic-segmentation
Last synced: 16 days ago
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LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs (CVPR 2023)
- Host: GitHub
- URL: https://github.com/dvlab-research/LargeKernel3D
- Owner: dvlab-research
- License: apache-2.0
- Created: 2022-06-17T08:29:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-16T05:09:35.000Z (over 1 year ago)
- Last Synced: 2024-08-01T03:41:37.294Z (3 months ago)
- Topics: 3d, nuscenes, object-detection, scannet, semantic-segmentation
- Language: Python
- Homepage: https://arxiv.org/abs/2206.10555
- Size: 2.34 MB
- Stars: 190
- Watchers: 6
- Forks: 8
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs (CVPR 2023)
This is the implementation of ***LargeKernel3D*** (CVPR 2023). Large kernels are important but expensive in 3D CNNs. We propose spatial-wise partition to conv enable 3D large kernels. High performance on 3D semantic segmentation & object detection.
For more details, please refer to:
**LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs [[Paper](https://arxiv.org/abs/2206.10555)]**
[Yukang Chen](https://scholar.google.com/citations?user=6p0ygKUAAAAJ&hl=en), [Jianhui Liu](https://scholar.google.com/citations?user=n1JW-jYAAAAJ&hl=en), [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=zh-CN), [Xiaojuan Qi](https://scholar.google.com/citations?user=bGn0uacAAAAJ&hl=en), [Jiaya Jia](https://scholar.google.com/citations?user=XPAkzTEAAAAJ&hl=en)
### Experimental results
| nuScenes Object Detection | Set | mAP | NDS | Download |
|------------------------------------------------------------------------------------------------------------------------------|:--------------:|:----:|:----:|:------------------------------:|
| [LargeKernel3D](object-detection/configs/nusc/voxelnet/nusc_centerpoint_voxelnet_0075voxel_fix_bn_z_largekernel3d_tiny.py) | val | 63.3 | 69.1 | [Pre-trained](https://drive.google.com/file/d/1qDCareDEyzElFMH0iPuMYkMVozI8qSGQ/view?usp=share_link) |
| [LargeKernel3D](object-detection/configs/nusc/voxelnet/nusc_centerpoint_voxelnet_0075voxel_fix_bn_z_largekernel3d_multimodal.py) | test | 65.4 | 70.6 | [Pre-trained](https://drive.google.com/file/d/1Cipmcq5PFyxObWkJPG9LPUNVnYsrlYBH/view?usp=share_link) [Submission](https://drive.google.com/file/d/1y2Km6rCb7PFBoe458cYL-H4Jh3yDeBe1/view?usp=share_link) |
| +test aug | test | 68.7 | 72.8 | [Submission](https://drive.google.com/file/d/15gYXgwE6XSIJhrnEFFvwKHXdPv9rK_PM/view?usp=share_link) |
| [LargeKernel3D-F](object-detection/configs/nusc/voxelnet/nusc_centerpoint_voxelnet_0075voxel_fix_bn_z_largekernel3d_multimodal.py) | test | - | - | [Pre-trained](https://drive.google.com/file/d/1MDSOGEtV0BZ_GCWDiedyLe9h1pi-lnnV/view?usp=share_link) |
| +test aug | test | 71.1 | 74.2 | [Submission](https://drive.google.com/file/d/1eQkQRA7YPAn6DuEh6oUA_VOv6vvs1csF/view?usp=share_link) |
| ScanNetv2 Semantic Segmentation | Set | mIoU | Download |
|-------------------------------------------------------------------------------------|:----:|:----:|:-----------------------------------------------------------------------------------------------------:|
| LargeKernel3D | val | 73.5 | [Pre-trained] |
| LargeKernel3D | test | 73.9 | [[Submission]](https://drive.google.com/file/d/18Xm9ZQukxXwR5qQDazICHD7Zhg48CpCw/view?usp=share_link) |
## Citation
If you find this project useful in your research, please consider citing:
```
@inproceedings{chen2023largekernel3d,
title={LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs},
author={Yukang Chen and Jianhui Liu and Xiangyu Zhang and Xiaojuan Qi and Jiaya Jia},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023}
}
```
## Acknowledgement
- This work is built upon [FocalsConv](https://github.com/dvlab-research/FocalsConv) for object detection.
- This work is built upon [Stratified-Transformer](https://github.com/dvlab-research/Stratified-Transformer) for semantic segmentation.
## Our Works in LiDAR-based 3D Computer Vision
- **VoxelNeXt (CVPR 2023)** [[Paper]](https://arxiv.org/abs/2303.11301) [[Code]](https://github.com/dvlab-research/VoxelNeXt) Fully Sparse VoxelNet for 3D Object Detection and Tracking.
- **Focal Sparse Conv (CVPR 2022 Oral)** [[Paper]](https://arxiv.org/abs/2204.12463) [[Code]](https://github.com/dvlab-research/FocalsConv) Dynamic sparse convolution for high performance.
- **Spatial Pruned Conv (NeurIPS 2022)** [[Paper]](https://arxiv.org/abs/2209.14201) [[Code]](https://github.com/CVMI-Lab/SPS-Conv) 50% FLOPs saving for efficient 3D object detection.
- **LargeKernel3D (CVPR 2023)** [[Paper]](https://arxiv.org/abs/2206.10555) [[Code]](https://github.com/dvlab-research/LargeKernel3D) Large-kernel 3D sparse CNN backbone.
- **SphereFormer (CVPR 2023)** [[Paper]](https://arxiv.org/abs/2303.12766) [[Code]](https://github.com/dvlab-research/SphereFormer) Spherical window 3D transformer backbone.
- [spconv-plus](https://github.com/dvlab-research/spconv-plus) A library where we combine our works into [spconv](https://github.com/traveller59/spconv).
- [SparseTransformer](https://github.com/dvlab-research/SparseTransformer) A library that includes high-efficiency transformer implementations for sparse point cloud or voxel data.
## License
This project is released under the [Apache 2.0 license](LICENSE).