This is a PyTorch implementation of the paper ELFNet: Evidential Local-global Fusion for Stereo Matching (ICCV 2023).
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.txtSCENE_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
|_ training
|_ disp_occ
|_ colored_0
|_ colored_1
|_2015
|_ training
|_ disp_occ_0
|_ image_2
|_ image_3
MIDDLEBURY
|_ trainingQ
|_ Motorcycle
|_ disp0GT.pfm
|_ disp1GT.pfm
|_ im0.png
|_ im1.png
|_ mask0nocc.png
|_ mask1nocc.png
sh scripts/elfnet_pretrain.shYou can download the checkpoint pretrained on Scene Flow Dataset from this Google Drive link. (Note that you don't need to untar the checkpoint.)
sh scripts/elfnet_test_sceneflow.sh
sh scripts/elfnet_test_kitti.sh
sh scripts/elfnet_test_middlebury.shIf 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}
}
We thank for the code implementation from PCWNet, STTR and Evidential-deep-learning.