README.md

HybridStereoNet

This repository contains the code for our MICCAI 2022 paper Deep Laparoscopic Stereo Matching with Transformers [arXiv]

Requirements

Environment

1. Python 3.8.*
2. CUDA 10.0
3. PyTorch
4. TorchVision

Install

Create a virtual environment and activate it.

conda create -n hybristereo python=3.8
conda activate hybristereo

The code has been tested with PyTorch 1.6 and Cuda 10.2.

conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.2 -c pytorch
conda install matplotlib path.py tqdm
conda install tensorboard tensorboardX
conda install scipy scikit-image opencv

Install Nvidia Apex

Follow the instructions here. Apex is required for mixed precision training. Please do not use pip install apex - this will not install the correct package.

Dataset

To evaluate/train our network, you will need to download the required datasets.

• SceneFlow

• SCARED 2019

• dVPN

Prediction

You can evaluate a trained model using prediction.sh for each dataset, that would help you generate *.png or *.pfm images correspoding to different datasets.

sh predict_scared.sh
sh predict_davinci.sh

Evaluate Tool

Use the file 'main_all.m' in eval folder to evaluate your model. Download our pre-trained model via this link: snapshot.

Acknowledgements

Citing

If you find this code useful, please consider to cite our work.

@inproceedings{cheng2022laparoscopic,
  title={Deep Laparoscopic Stereo Matching with Transformers},
  author={Cheng, Xuelian and Zhong, Yiran and Harandi, Mehrtash and Drummond, Tom and Wang, Zhiyong and Ge, Zongyuan},
  booktitle={MICCAI},
  year={2022}
}