Prior Guided Dropout for Robust Visual Localization in Dynamic Environments
Zhaoyang Huang, Yan Xu, Jianping Shi, Xiaowei Zhou, Hujun Bao, Guofeng Zhang
Licensed under the CC BY-NC-SA 4.0 license, see LICENSE.
If you find this code useful for your research, please cite our paper
@inproceedings{huang2019prior,
title={Prior Guided Dropout for Robust Visual Localization in Dynamic Environments},
author={Huang, Zhaoyang and Xu, Yan and Shi, Jianping and Zhou, Xiaowei and Bao, Hujun and Zhang, Guofeng},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={2791--2800},
year={2019}
}
PGD-MapNet uses Conda to setup the environment
conda env create -f environment.yml
conda activate pgd-mapnet
The data is processed as suggested in geomapnet.
The dynamic information computed from Mask_RCNN is stored in datainfo.
The files should be put into the corresponding root dir of each scene.
cd experiments
bash runattmapnet.sh
cp logs/exp_beta[-3.0]gamma[-3.0]batch_size[64]model[attentionmapnet]mask_sampling[True]sampling_threshold[0.2]color_jitter[0.0]uniform_sampling[False]mask_image[False]dataset[RobotCar]scene[full]/config.json admapfull.json
bash run_eval.sh
Our code partially builds on geomapnet.
The work is affliated with ZJU-SenseTime Joint Lab of 3D Vision, and its intellectual property belongs to SenseTime Group Ltd.