Pytorch implementation of ICRA 2020 paper "360° Stereo Depth Estimation with Learnable Cost Volume"
project page | paper | dataset
This is the implementation of our ICRA 2020 paper "360° Stereo Depth Estimation with Learnable Cost Volume" by Ning-Hsu Wang
# SETUP REPO
>> git clone https://github.com/albert100121/360SD-Net.git
>> cd 360SD-Net
>> mkdir output
>> cd conda_env
>> conda create --name 360SD-Net python=2.7
>> conda activate 360SD-Net
>> conda install --file requirement.txt
# DOWNLOAD MP3D Dataset
>> cd ./data
# reqest download MP3D Dataset
>> unzip MP3D Dataset
# request download SF3D Dataset
>> unzip SF3D Dataset
# MP3D Dataset
./data/
|--MP3D/
|--train/
|--image_up/
|--image_down/
|--disp_up/
|--val/
|--image_up/
|--image_down/
|--disp_up/
|--test/
|--image_up/
|--image_down/
|--disp_up/
# SF3D Dataset
./data/
|--SF3D/
|--train/
|--image_up/
|--image_down/
|--disp_up/
|--val/
|--image_up/
|--image_down/
|--disp_up/
|--test/
|--image_up/
|--image_down/
|--disp_up/
# For MP3D Dataset
>> python main.py --datapath data/MP3D/train/ --datapath_val data/MP3D/val/ --batch 8
# For SF3D Dataset
>> python main.py --datapath data/SF3D/train/ --datapath_val data/SF3D/val/ --batch 8 --SF3D
# For MP3D Dataset
>> python testing.py --datapath data/MP3D/test/ --checkpoint checkpoints/MP3D_checkpoint/checkpoint.tar --outfile output/MP3D
# For SF3D Dataset
>> python testing.py --datapath data/SF3D/test/ --checkpoint checkpoints/SF3D_checkpoint/checkpoint.tar --outfile output/SF3D
# For Real World Data
>> python testing.py --datapath data/realworld/ --checkpoint checkpoints/Realworld_checkpoint/checkpoint.tar --real --outfile output/realworld
# For small inference
>> python testing.py --datapath data/inference/MP3D/ --checkpoint checkpoints/MP3D_checkpoint/checkpoint.tar --outfile output/small_inference
>> python utils/disp2de.py --path PATH_TO_DISPARITY
@article{wang2019360sdnet,
title={360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume},
author={Ning-Hsu Wang and Bolivar Solarte and Yi-Hsuan Tsai and Wei-Chen Chiu and Min Sun},
journal={arXiv preprint arXiv:1911.04460},
year={2019}
}