DAEVI (Depth-Aware Endoscopic Video Inpainting)

The code repository for Depth-Aware Endoscopic Video Inpainting. The pre-trained model for our paper could be found at here. The ArXiv version paper can be found at here.

If you encounter any difficulty in implementing our work, please feel free to contact me (francis.xiatian.zhang@ieee.org).

Training

python train.py --model DAEVI --config {Your Config File Path}.json

Inference

python test.py  --gpu 0 --overlaid --output results/DAEVI_Output/ --frame datasets/EndoSTTN_dataset/JPEGImages --mask datasets/EndoSTTN_dataset/Annotations --model DAEVI -c release_model/DAEVI_24g -cn 20 --zip --ref_num 10

References

• Repository: Endo-STTN.

Citing

If you find this work useful, please consider our paper to cite:

@inproceedings{zhang24Depth,
 author={Zhang, Francis Xiatian and Chen, Shuang and Xie, Xianghua and Shum, Hubert P. H.},
 booktitle={Proceedings of the 2024 International Conference on Medical Image Computing and Computer Assisted Intervention},
 series={MICCAI '24},
 title={Depth-Aware Endoscopic Video Inpainting},
 year={2024},
 publisher={Springer},
 location={Marrakesh, Morocco},
}