Representing 3D sparse map points and lines for camera relocalization
Bach-Thuan Bui, Huy-Hoang Bui, Dinh-Tuan Tran, Joo-Ho Lee
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Todo list:
- release code
- add to work with custom data
Python 3.9 + required packages
git clone https://github.com/ais-lab/pl2map.git
cd pl2map
git submodule update --init --recursive
conda create --name pl2map python=3.9
conda activate pl2map
# Refer to https://pytorch.org/get-started/previous-versions/ to install pytorch compatible with your CUDA
python -m pip install torch==1.12.0 torchvision==0.13.0
python -m pip install -r requirements.txt
Please run the provided scripts to prepare and download the data which has been preprocessed by running:
7scenes
./prepare_scripts/seven_scenes.sh
Cambridge Landmarks
./prepare_scripts/cambridge.sh
Indoor-6
./prepare_scripts/indoor6.sh
Please download the pre-trained models by running:
./prepare_scripts/download_pre_trained_models.sh
For example, to evaluate KingsCollege scene:
python runners/eval.py --dataset Cambridge --scene KingsCollege -expv pl2map
python runners/train.py --dataset Cambridge --scene KingsCollege -expv pl2map_test
If you use this code in your project, please consider citing the following paper:
@article{bui2024representing,
title={Representing 3D sparse map points and lines for camera relocalization},
author={Bui, Bach-Thuan and Bui, Huy-Hoang and Tran, Dinh-Tuan and Lee, Joo-Ho},
booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2024}
}
This code is built based on Limap, and LineTR. We thank the authors for their useful source code.