• ICRA2020 Paper
• ICRA2020 Presentation

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

• Clone Repo

git clone https://github.com/sejong-rcv/MDII
cd MDII

• Prerequisite
  • nvidia-docker
• Option
  • visdom port number

nvidia-docker run -it -v $PWD:/workspace -p {port}:8888 -e NVIDIA_VISIBLE_DEVICES=all handchan/mdii /bin/bash

if you have any problem with downloading the docker image, check this repository : https://hub.docker.com/r/handchan/mdii/tags

• Download Dataset

cd MDII
curl http://multispectral.sejong.ac.kr/ICRA2020_MDII/ICRA_MDII.tar.gz -o ICRA_MDII.tar.gz
tar -xzvf ICRA_MDII.tar.gz

• we support the pre-processed dataset.If you want to check the original dataset, refer to the following papers.
  • All-day Vision Dataset (CVPRW 2015)
  • All-day Vision Dataset (TITS 2018)

• Running train.py

python train.py --name MDII --model MDII_gan --dataroot ./ICRA_MDII --gpu_ids GPU_NUM  --no_dropout --no_AA_BB

• Running feat_c.py , make .npz file

### Convert train img to MDII
python feat_c.py --epoch {Epoch} --gpu_ids {GPU_NUM} --name MDII \ 
  --dataroot ./ICRA_MDII --no_dropout --model MDII_gan --phase train --eval
### Convert test img to MDII
python feat_c.py --epoch {Epoch} --gpu_ids {GPU_NUM} --name MDII \
  --dataroot ./ICRA_MDII --no_dropout --model MDII_gan --phase test --eval

• Using Matlab vlfeat code. run rank.py
  • Download VLFeat (our version is vlfeat-0.9.21)
  • Replace {vlfeat dir}/apps/recognition/ to recognition_MDII
  • Place your convert result name as {vlfeat dir}/MDII

    cd {vlfeat dir}
    ln -s {result dir} MDII (ex. ../../result/images/ICRA_MDII/{checkpoint name}/{epoch}/)
    # {vlfeat dir}
    # ├── apps
    # │   └── recognition
    # ├── data
    # │   ├── MDII -> ../../result/images/ICRA_MDII/{checkpoint name}/{epoch}/
    # │   │   ├── test
    # │   │   │   ├── rgb
    # │   │   │   ├── thr
    # │   │   ├── train
    # │   │   │   ├── rgb
    # │   │   │   ├── thr
    # ├──
    # ...

  • Run the Matlab code {vlfeat dir}/apps/recognition/experiments.m
  • Run the python code rank.py {workspace/rank.py}

  python rank.py --cache_path ./{vlfeat dir}/data_MDII_0604_200epoch/ex-MDII-vlad-aug
  # You can see the detail in python rank.py --help

@INPROCEEDINGS{ICRA2020,
  author = {Daechan Han*, YuJin Hwang*, Namil Kim, Yukyung Choi},
  title = {Multispectral Domain Invariant Image for Retrieval-based Place Recognition},
  booktitle = {International Conference on Robotics and Automation(ICRA)},
  year = {2020}
}