search

sshan-zhao / ACMNet

Adaptive Context-Aware Multi-Modal Network for Depth Completion
66 stars 11 forks source link

readme

ACMNet

This is the Pytorch implementation of our work on depth completion.

S. Zhao, M. Gong, H. Fu and D. Tao. Adaptive Context-Aware Multi-Modal Network for Depth Completion. (IEEE Trans. Image Process.) Arxiv(Early Version) IEEE(Final Version)

Environment

  1. Python 3.6
  2. PyTorch 1.2.0
  3. CUDA 10.0
  4. Ubuntu 16.04
  5. Opencv-python
  6. pip install pointlib/.

Datasets

KITTI

Prepare the dataset according to the datalists (*.txt in datasets)

datasets
|----kitti 
    |----depth_selection 
        |----val_selection_cropped
            |----...
        |----test_depth_completion_anonymous   
            |----...     
    |----rgb     
        |----2011_09_26
        |----...  
    |----train  
        |----2011_09_26_drive_0001_sync
        |----...   
    |----val   
        |----2011_09_26_drive_0002_sync
        |----...

Training

run

bash run_train.sh

Test

run

bash run_eval.sh (sval.txt for selected_validation, val for validation) or bash run_test.sh (for submission)

Citation

@article{zhao2021adaptive,
  title={Adaptive context-aware multi-modal network for depth completion},
  author={Zhao, Shanshan and Gong, Mingming and Fu, Huan and Tao, Dacheng},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  publisher={IEEE}
}

Contact

Shanshan Zhao: szha4333@uni.sydney.edu.au or sshan.zhao00@gmail.com