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TorchSSC

license PyTorch-1.0.0

Implement some state-of-the-art methods of Semantic Scene Completion (SSC) task in PyTorch.

Highlights:

  • Distributed training
  • Easy-to-modify benchmark code
  • High performance

News

Performance

NYU

Method Resolution Trained on SC IoU SSC mIoU
SSCNet (240, 60) NYU 55.1 24.7
VVNetR-120 (120, 60) NYU+SUNCG 61.1 32.9
DDRNet (240, 60) NYU 61.0 30.4
ForkNet (80, 80) NYU 63.4 37.1
CCPNet (240, 240) NYU 63.5 38.5
SketchAwareSSC (Ours) (60, 60) NYU 71.3 41.1

Data Preparation && Environment Installation

Pretrained ResNet-50

Please download the pretrained ResNet-50 and then put it into ./DATA/pytorch-weight.

Source Link
BaiDu Cloud Link: https://pan.baidu.com/s/1wS1TozvS3cBdutsXRWUmUw Key: 4g9u
Google Drive https://drive.google.com/drive/folders/121yZXBZ8wV77WRXRur86YBA4ifJEhsJQ?usp=sharing

NYU Depth V2

Please download NYU dataset and then put it into ./DATA/NYU.

Source Link
BaiDu Cloud Link: https://pan.baidu.com/s/1GfWqAbsfMp3NOjFcEnL54A Key: v5ta
Google Drive https://drive.google.com/drive/folders/121yZXBZ8wV77WRXRur86YBA4ifJEhsJQ?usp=sharing

Environment Installation

Please refer to this documentation

3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior

Training and Inference

Training

Training on NYU Depth V2:

$ cd ./model/sketch.nyu
$ export NGPUS=2
$ python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py -p 10097
  • -p is the port number. It is about the distributed training. If you run more than one experiments in the same machine, you should set different ports for them.
  • The tensorboard file is saved in sketch.nyu/log/tb/ directory.

Inference

Inference on NYU Depth V2:

$ cd ./model/sketch.nyu
$ python eval.py -e 200-250 -d 0-1 --save_path results
  • Here, 200-250 means we evaluate on checkpoints whose ID is in [200, 250], such as epoch-200.pth, epoch-249.pth, etc.
  • The SSC predictions will be saved in results/ and results_sketch/, the former stores the SSC predictions and the latter stores sketch preditcions. Performance will be written to log/*.log. You will expect 0.411@SSC mIoU and 0.713@SC IoU.

Citation

If you find this work useful in your research, please consider cite:

@InProceedings{Chen_2020_SketchAwareSSC,
    author = {Chen, Xiaokang and Lin, Kwan-Yee and Qian, Chen and Zeng, Gang and Li, Hongsheng},
    title = {3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}

Acknowledgement

Thanks TorchSeg for their excellent project!

TODO

  • Code on more datasets (NYUCAD/SUNCG).
  • More SSC models.