- 2023-05 Preliminary codes are released.
- 2023-02 Our SCPNet is accepted by CVPR 2023 (Highlight)!
- 2022-11 Our method ranks 1st in SemanticKITTI Semantic Scene Completion Challenge, with mIoU=36.7.
- SCPNet is comprised of a novel completion sub-network without an encoder-decoder structure and a segmentation sub-network obtained by replacing the cylindrical partition of Cylinder3D with conventional cubic partition.
- PyTorch >= 1.10
- pyyaml
- Cython
- tqdm
- numba
- Numpy-indexed
- torch-scatter
- spconv (tested with spconv==1.0 and cuda==11.3)
./
├──
├── ...
└── path_to_data_shown_in_config/
├──sequences
├── 00/
│ ├── velodyne/
| | ├── 000000.bin
| | ├── 000001.bin
| | └── ...
│ └── labels/
| ├── 000000.label
| ├── 000001.label
| └── ...
│ └── voxels/
| ├── 000000.bin
| ├── 000000.label
| ├── 000000.invalid
| ├── 000000.occluded
| ├── 000001.bin
| ├── 000001.label
| ├── 000001.invalid
| ├── 000001.occluded
| └── ...
├── 08/ # for validation
├── 11/ # 11-21 for testing
└── 21/
└── ...
We take evaluation on the SemanticKITTI test set (single-scan) as example.
- Download the pre-trained models and put them in
./model_load_dir
. - Set val_data_loader>imageset: “test” in the configuration file
config/semantickitti-multiscan.yaml
. - Generate predictions on the SemanticKITTI test set.
CUDA_VISIBLE_DEVICES=0 python -u test_scpnet_comp.py
The model predictions will be saved in ./out_scpnet/test
by default.
- Set val_data_loader>imageset: “test” in the configuration file
config/semantickitti-multiscan.yaml
. - train the network by running the train script
CUDA_VISIBLE_DEVICES=1 python -u train_scpnet_comp.py
If you use the codes, please cite the following publication:
@inproceedings{scpnet,
title = {SCPNet: Semantic Scene Completion on Point Cloud},
author = {Xia, Zhaoyang and Liu, Youquan and Li, Xin and Zhu, Xinge and Ma, Yuexin and Li, Yikang and Hou, Yuenan and Qiao, Yu},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2023}
}
We thanks for these codebases, including Cylinder3D, PVKD and spconv.