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Changhee Won, Jongbin Ryu and Jongwoo Lim "End-to-End Learning for Omnidirectional Stereo Matching with Uncertainty Prior", in TPAMI (ICCV 19)

License: GNU General Public License v3.0

Python 100.00%

omnimvs-pytorch's Introduction

OmniMVS

This repository contains python codes for paper, "End-to-End Learning for Omnidirectional Stereo Matching with Uncertainty Prior" (TPAMI).

Contact: Changhee Won ([email protected])

Prerequisites

List of code/library dependencies

Pytorch (tested on 1.5.1)
pip install numpy scipy matplotlib pyyaml EasyDict scikit-image

How to run

Test (run_test_omnimvs.py)

Pretrained weights:
- [omnimvs_plus_ft.pt]
- [tiny_plus_ft.pt]
Set arguments in the script
Run python run_test_omnimvs.py [path_to_pt_file] [dbname]

Example results

Dataset

You can download the synthetic datasets in the project page.

The directory structure should be like this:

[db_root]/[dbname]/[cam%d]/[%05d.png]
                  /omnidepth_gt_640/%05d.tiff  # not necessary
                  /config.yaml
                   ...
(e.g.)
data/sunny/
          /cam1/
          /cam2/
          /cam3/
          /cam4/
          /omnidepth_gt_640/
          /config.yaml

Citation & Acknowledgement

We founded a start-up company MultiplEYE co. ltd. based on this research.

@article{won2020end,
    title={End-to-End Learning for Omnidirectional Stereo Matching with Uncertainty Prior},
    author={Won, Changhee and Ryu, Jongbin and Lim, Jongwoo},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)},
    year={2020},
}

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