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SBIN: A stereo disparity estimation network using binary convolutions

This is the implementation of our paper"SBIN: A stereo disparity estimation network using binary convolutions"


Results

Model Dataset EPE Err > 3 Exp ID
normalbin_res_prelu_avgpool Kitti2012 2.0979 0.1370 1

Reproduce best results (Training in using docker)

Requirements

  • docker
  • nvidia-docker
  • Download Kitti2012, Kitti2015 and Sceneflow datasets
    • Datasets must be inside the folder datasets in the root folder of this repository
    • Datasets
      • kitti2012
      • kitti2015
      • sceneflow

Log and checkpoints are created on the folder output

Docker (training on x64 arch)

./docker/launch.sh

# Sceneflow training
python experiments.py train 2

# Kitti2012 training 
python experiments.py train 1

Further experiments

To add new experiments, check the experiments folder of directly use the cli.py, after reading the command line help

Citation

@article{Aguilera_2022,
title={SBIN: A stereo disparity estimation network using binary convolutions},
volume={20},
url={https://latamt.ieeer9.org/index.php/transactions/article/view/5909},
number={4}, journal={IEEE Latin America Transactions},
author={Aguilera, Cristhian Alejandro},
year={2022},
month={Jan.}, 
pages={693–699} }

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