ELFNet

This is a PyTorch implementation of the paper ELFNet: Evidential Local-global Fusion for Stereo Matching (ICCV 2023).

Environment

You can create a conda environment with following commands.

conda create env -n elfnet python=3.8
conda activate elfnet
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt

Data Preparation

Scene Flow

SCENE_FLOW
    |_ frames_finalpass
        |_ TRAIN
            |_ A
                |_0000
    |_ disparity
        |_ TRAIN
            |_ A
                |_0000
    |_ occlusion
        |_ TRAIN
            |_ left

You may need to run ./utilities/subsample_sceneflow.py to down sample the data.

KITTI 2012 & KITTI 2015

KITTI
    |_2012
          |_ training
              |_ disp_occ
              |_ colored_0
              |_ colored_1
    |_2015
          |_ training
              |_ disp_occ_0
              |_ image_2
              |_ image_3

Middlebury 2014

MIDDLEBURY
    |_ trainingQ
        |_ Motorcycle
            |_ disp0GT.pfm
            |_ disp1GT.pfm
            |_ im0.png
            |_ im1.png
            |_ mask0nocc.png
            |_ mask1nocc.png

Experiments

Training

sh scripts/elfnet_pretrain.sh

You can download the checkpoint pretrained on Scene Flow Dataset from this Google Drive link. (Note that you don't need to untar the checkpoint.)

Evaluation

sh scripts/elfnet_test_sceneflow.sh
sh scripts/elfnet_test_kitti.sh
sh scripts/elfnet_test_middlebury.sh

Citation

If you find our work useful or provides some new insights😊, please consider citing our paper using the following BibTeX entry.

@article{lou2023elfnet,
  title={ELFNet: Evidential Local-global Fusion for Stereo Matching},
  author={Lou, Jieming and Liu, Weide and Chen, Zhuo and Liu, Fayao and Cheng, Jun},
  journal={arXiv preprint arXiv:2308.00728},
  year={2023}
}

Acknowledgements

We thank for the code implementation from PCWNet, STTR and Evidential-deep-learning.