00mjk / omnimvs_pytorch

An unofficial PyTorch implementation of ICCV 2019 paper "OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching"

OmniMVS PyTorch

An unofficial PyTorch implementation of ICCV 2019 paper "OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching".

Requirements

You need Python 3.6 or later for f-Strings.

Python libraries:

• PyTorch >= 1.3.1
• torchvision
• SciPy >= 1.4.0 (scipy.spatial.transform)
• OpenCV
• tensorboard
• tqdm
• Open3D >= 0.8 (only for visualization)

Setup

Clone repository

Please run the following command. On the first line, Python OcamCalib undistortion library is installed for undistortion of Davide Scaramuzza's OcamCalib camera model.

pip install git+git://github.com/matsuren/ocamcalib_undistort.git
git clone https://github.com/matsuren/omnimvs_pytorch.git

Download dataset

Download OmniThings in Omnidirectional Stereo Dataset from here. After extraction, please put the dataset folder in the following places.

omnimvs_pytorch/
├── ...
└── datasets/
   └── omnithings/
        ├── cam1/
        ├── cam2/
        ├── cam3/
        ├── cam4/
        ├── depth_train_640/
        ├── ocam1.txt
        ├── ...

❗Attention❗
For some reasons, some filenames are inconsistent in OmniThings. For instance, the first image is named 00001.png in cam1, but, it is named 0001.png for cam2, cam3, and cam4. So please rename 0001.png, 0002.png, and 0003.png so that they have five-digit numbers.

Training

Run with default parameter (input image size: 500x480, output depth size: 512x256, disparity: 64).

python train.py ./datasets/omnithings

These default parameters are smaller than the ones reported in their paper due to GPU memory limitation. You can change parameters by arguments (-h option for details).

A pre-trained model (ndisp=48) is available here.

Results

Predictions on OmniHouse after training on OmniThings (ndips=48).