- [2022-11-19] The paper has been published online! Check here to get it.
- [2022-11-8] Our paper has been accpeted by ISPRS (International Society for Photogrammetry and Remote Sensing) !
- [2022-5-27] Submit the manuscript to the journal.
HL-MRF can automatically perform the point-cloud-based registration for large-scale TLS scans without any artificial target. The main focus of our framework lies in the efficiency of processing large-scale TLS scans and being able to tolerate pairwise registration failure for robustness. The proposed hierarchical registration strategy and the loop-based coarse registration method deal with them respectively.
Two key features in HL-MRF are:
- An internal scan-block and block-to-block registration strategy to hierarchically align point clouds of large-scale TLS scans.
- A loop-based coarse registration method to remove grossly wrong pairwise registration results caused by ambiguous scanning content.
The code was tested on Windows (Visual Studio 2021) and Ubuntu 18.04
- CMake version 2.8 or above
- PCL version 1.11.0 (other versions may also work)
- Boost version 1.65 or above
Clone this repository
git clone https://github.com/WuHao-WHU/HL-MRF.git
cd HL-MRF
mkdir build
cd build
cmake ..
make
On Ubuntu
cd ..
sh script/run.sh
On Windows, you can refer to run.sh to pass parameters in Visual Studio
#./script/run.sh
# TLS input path
path_dir=../scans/;#input directory
out_dir=../result;#output directory
#parameters
block_size=5; # block size for hierarchical regisration (default: 5)
downsample_size=0.1; # downsample size of input TLS scans for coarse registration (default: 0.1 m)
downsample_size_icp=0.1; # distance of searching corresponding point in icp (default: the same as ${downsample_size})
lum_iter=3; # number of iteration for lum optimization (default: 3)
t_MCS=10; # threshold of MCS (default: 10)
number_of_threads=16; # number of threads you use (default: 16)
visualize_each_block=0; # visualize each scan-block (default: 0)
You can test on the open-source TLS dataset: WHU-TLS, Robotic 3D Scan datasets, ETH Dataset.
You can also use your own data and edit the path_dir in run.sh.
The framework supports *.ply, *.pcd formats of point cloud data. You may need to transform other formats to the supported formats.
If you find out our code useful in your research, please cite
@article{WU202365,
title = {A hierarchical multiview registration framework of TLS point clouds based on loop constraint},
author = {Hao Wu and Li Yan and Hong Xie and Pengcheng Wei and Jicheng Dai},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {195},
pages = {65-76},
year = {2023}
}
@article{Yan2022,
archivePrefix = {arXiv},
arxivId = {2205.07404},
author = {Yan, Li and Wei, Pengcheng and Xie, Hong and Dai, Jicheng and Wu, Hao and Huang, Ming},
eprint = {2205.07404},
pages = {1--16},
title = {A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration},
year = {2022}
}
@article{theiler2015globally,
title={Globally consistent registration of terrestrial laser scans via graph optimization},
author={Theiler, Pascal Willy and Wegner, Jan Dirk and Schindler, Konrad},
journal={ISPRS journal of photogrammetry and remote sensing},
volume={109},
pages={126--138},
year={2015},
publisher={Elsevier}
}
Hao Wu (吴豪)，Pengcheng Wei (韦朋成)
Email: haowu2021@whu.edu.cn
Do not hesitate to contact the authors if you have any question or find any bugs.
