RomniStereo

The Pytorch code for our following paper

RomniStereo: Recurrent Omnidirectional Stereo Matching, RA-L 2024 (pdf)

Hualie Jiang, Rui Xu, Minglang Tan and Wenjie Jiang [Insta360]

Preparation

Installation

Create the environment

conda create -n romnistereo python=3.8

Install pytorch

conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=10.2 -c pytorch  # for cuda 10
conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch  # for cuda 11

Then install other requirements

pip install -r requirements.txt

Download Datasets

Please refer to download.sh for downloading datasets

Training

Train on OmniThings

python train.py --name romnistereoC --dbname omnithings --base_channel C --mixed_precision --total_epochs 30 
# C can be 4, 8, 32, 64

Finetune on Omnihouse and Sunny

python train.py --name romnistereoC_ft --dbname omnihouse sunny --base_channel C --mixed_precision --total_epochs 16 --pretrain_ckpt checkpoints/romnistereoC/romnistereoC_e29.pth
# C can be 4, 8, 32, 64

Evaluation

The pretrained models of our paper is available on Google Drive.

python eval.py --dbname omnithings/omnihouse/sunny/cloudy/sunet --restore_ckpt models/romnistereoC[_ft].pth --save_result 
# C can be 4, 8, 32, 64

Test on real samples

python test.py --dbname itbt_sample/real_indoor_sample --restore_ckpt models/romnistereoC[_ft].pth --vis --save_result  
# C can be 4, 8, 32, 64

Acknowledgements

The project borrows codes from OmniMVS and RAFT-Stereo. Many thanks to their authors.

Citation

Please cite our paper if you find our work useful in your research.

@inproceedings{jiang2024romnistereo,
  title={RomniStereo: Recurrent Omnidirectional Stereo Matching},
  author={Jiang, Hualie and Xu, Rui and Tan, Minglang and Jiang, Wenjie},
  booktitle={IEEE Robotics and Automation Letters},
  year={2024}
}