BGNet

This repository contains the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

Environment

1. Python 3.6.*
2. CUDA 10.1
3. PyTorch 1.7.1
4. TorchVision 0.8.2

Dataset

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

• SceneFlow
• IRS
• KITTI2015
• KITTI2012
• Middlebury 2014

Pretrained model

We provide seven pretrained model under the folder models .

Evaluation

We provided a script to get the kitti benchmark result,check predict.sh for an example usage.

Prediction

We support predicting on any rectified stereo pairs. predict_sample.py provides an example usage.

Acknowledgements

Part of the code is adopted from the previous works: DeepPruner, GwcNet, GANet and AANet. We thank the original authors for their contributions.

Citing

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

@inproceedings{xu2021bilateral,
  title={Bilateral Grid Learning for Stereo Matching Networks},
  author={Xu, Bin and Xu, Yuhua and Yang, Xiaoli and Jia, Wei and Guo, Yulan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1--10},
  year={2021}
}