Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)
Our model achieves state-of-the-art performance on three benchmarks, i.e., ranks 1st in Waymo 3D Semantic Segmentation Challenge (the "Cylinder3D" and "Offboard_SemSeg" entities), ranks 1st in SemanticKITTI LiDAR Semantic Segmentation Challenge (single-scan, the "Point-Voxel-KD" entity), ranks 2nd in SemanticKITTI LiDAR Semantic Segmentation Challenge (multi-scan, the "PVKD" entity). Our trained model has been used in one NeurIPS 2022 submission! Do not hesitate to use our trained models!
- PyTorch >= 1.2
- yaml
- tqdm
- numba
- Cython
- torch-scatter
- nuScenes-devkit (optional for nuScenes)
- spconv (tested with spconv==1.2.1 and cuda==10.2)
./
├──
├── ...
└── path_to_data_shown_in_config/
├──sequences
├── 00/
│ ├── velodyne/
| | ├── 000000.bin
| | ├── 000001.bin
| | └── ...
│ └── labels/
| ├── 000000.label
| ├── 000001.label
| └── ...
├── 08/ # for validation
├── 11/ # 11-21 for testing
└── 21/
└── ...
./
├──
├── ...
└── path_to_data_shown_in_config/
├──v1.0-trainval
├──v1.0-test
├──samples
├──sweeps
├──maps
./
├──
├── ...
└── path_to_data_shown_in_config/
├──first_return
├──second_return
We take evaluation on the SemanticKITTI test set (single-scan) as example.
-
Download the pre-trained models and put them in
./model_load_dir. -
Generate predictions on the SemanticKITTI test set.
CUDA_VISIBLE_DEVICES=0 python -u test_cyl_sem_tta.py
We perform test-time augmentation to boost the performance. The model predictions will be saved in ./out_cyl/test by default.
- Convert label number back to the original dataset format before submitting:
python remap_semantic_labels.py -p out_cyl/test -s test --inverse
cd out_cyl/test
zip -r out_cyl.zip sequences/
- Upload out_cyl.zip to the SemanticKITTI online server.
CUDA_VISIBLE_DEVICES=0 python -u train_cyl_sem.py
Remember to change the imageset of val_data_loader to val in semantickitti.yaml. Currently, we only support vanilla training.
- SemanticKITTI test set (single-scan):
| Model | Reported | Reproduced | Gain | Weight |
|---|---|---|---|---|
| SPVNAS | 66.4% | -- | -- | -- |
| Cylinder3D | 68.9% | 71.8% | 2.9% | cyl_sem_1.0x_71_8.pt |
| Cylinder3D_0.5x | 71.2% | 71.4% | 0.2% | cyl_sem_0.5x_71_2.pt |
- SemanticKITTI test set (multi-scan):
| Model | Reported | Reproduced | Gain | Weight |
|---|---|---|---|---|
| Cylinder3D | 52.5% | --% | --% | -- |
| Cylinder3D_0.5x | 58.2% | 58.4% | --% | cyl_sem_ms_0.5x_58_4.pt |
- Waymo test set:
| Model | Reported | Reproduced | Gain | Weight |
|---|---|---|---|---|
| Cylinder3D | 71.18% | 71.18% | -- | -- |
| Cylinder3D_0.5x | -- | -- | -- | -- |
- nuScenes val set:
| Model | Reported | Reproduced | Gain | Weight |
|---|---|---|---|---|
| Cylinder3D | 76.1% | -- | -- | -- |
| Cylinder3D_0.5x | 76.0% | 75.7% | -0.3% | cyl_nusc_0.5x_75_7.pt |
If you use the codes, please cite the following publications:
@inproceedings{Hou_2022_CVPR,
title = {Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation},
author = {Hou, Yuenan and Zhu, Xinge and Ma, Yuexin and Loy, Chen Change and Li, Yikang},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
pages = {8479-8488}
year = {2022},
}
@inproceedings{zhu2021cylindrical,
title={Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation},
author={Zhu, Xinge and Zhou, Hui and Wang, Tai and Hong, Fangzhou and Ma, Yuexin and Li, Wei and Li, Hongsheng and Lin, Dahua},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={9939--9948},
year={2021}
}
@article{zhu2021cylindrical-tpami,
title={Cylindrical and Asymmetrical 3D {C}onvolution {N}etworks for LiDAR-based Perception},
author={Zhu, Xinge and Zhou, Hui and Wang, Tai and Hong, Fangzhou and Li, Wei and Ma, Yuexin and Li, Hongsheng and Yang, Ruigang and Lin, Dahua},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2021},
publisher={IEEE}
}
This repo is built upon the awesome Cylinder3D.