3DCDNet: An End-to-end Point-based Method and A New Dataset for Street Level Point Cloud Change Detection
July 31, 2023 ยท View on GitHub
Requirement
python 3.7.4
torch 1.8.10
visdom 0.1.8.9
torchvision 0.9.0
SLPCCD Dataset
This dataset is developed from SHREC2O21 (T. Ku, S. Galanakis, B. Boom et al., SHREC 2021: 3D Point cloud change detection for street scenes, Computers & Graphics, https://doi.org/10.1016/j.cag.2021.07.004). It is a new 3D change detection benchmark dataset and aims to provide opportunities for researchers to develop novel 3D change detection algorithms. The dataset is available at [Google Drive] and [Baiduyun] (the password is: 8epz).
Pretrained Model
The pretrained model for SLPCCD is available at [Google Drive] and [Baiduyun] (the password is: 8epz).
Test
Before test, please download datasets and pretrained models. Change path to your data path in configs.py. Copy pretrained models to folder './outputs/best_weights', and run the following command:
cd 3DCDNet_ROOT
python test.py
Training
Before training, please download datasets and revise dataset path in configs.py to your path.
cd 3DCDNet_ROOT
python -m visdom.server
python train.py
To display training processing, open 'http://localhost:8097' in your browser.
Experiments on Urb3DCD dataset
The experiments on Urb3DCD dataset can be found from this link.
Citing 3DCDNet
If you use this repository or would like to refer the paper, please use the following BibTex entry.## Citing TransCD
@ARTICLE{10184135,
author={Wang, Zhixue and Zhang, Yu and Luo, Lin and Yang, Kai and Xie, Liming},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={An End-to-End Point-Based Method and a New Dataset for Street-Level Point Cloud Change Detection},
year={2023},
volume={61},
number={},
pages={1-15},
doi={10.1109/TGRS.2023.3295386}}
Reference
-T. Ku, S. Galanakis, B. Boom et al., SHREC 2021: 3D Point cloud change detection for street scenes, Computers & Graphics, https://doi.org/10.1016/j.cag.2021.07.004
-HU, Qingyong, et al. Randla-net: Efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020. p. 11108-11117.
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