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DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization

This is our Pytorch implementation for DASGIL (paper) by Hanjiang Hu, Zhijian Qiao and Ming Cheng. The work has been published in IEEE Transactions on Image Processing (TIP).

<img src='img/overview.png' align="center" width=666 alt="Text alternative when image is not available">

Prerequisites

Getting Started

Installation

pip install -r requirements.txt
git clone https://github.com/HanjiangHu/DASGIL.git

Training

KITTI and Virtual KITTI 2 dataset are used to train the model, while Extended CMU-Seasons dataset is used to test. The datasets involved in this paper are well organized HERE. Please uncompress it under the root path. Our pretrained models with FD and CD are found HERE. Please uncompress it under ./checkpoints.

python train.py --name DASGIL_FD
python train.py --name DASGIL_FD --continue_train --which_epoch 5

Testing

python test.py --name DASGIL_FD --which_epoch 5

Results

The test results will be saved to ./output. The txt results will be merged into a single txt file for all the slices and submitted to the official benchmark website.

Our DASGIL-FD results and DASGIL-CD results could be found on the Extended CMU-Seasons benchmark website.

Other Details

If you use this code in your own work, please cite:

H. Hu, Zhijian Qiao, M. Cheng, Z. Liu and H. Wang ā€DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localizationā€,

@ARTICLE{hu2020dasgil,
  author={H. {Hu} and Z. {Qiao} and M. {Cheng} and Z. {Liu} and H. {Wang}},
  journal={IEEE Transactions on Image Processing}, 
  title={DASGIL: Domain Adaptation for Semantic and Geometric-Aware Image-Based Localization}, 
  year={2021},
  volume={30},
  number={},
  pages={1342-1353},
  doi={10.1109/TIP.2020.3043875}}