A similar version of GraphSfM based on OpenMVG has been released in: https://github.com/AIBluefisher/EGSfM.
Our Structure from Motion approach, named Graph Structure from Motion (GraphSfM), is aimed at large scale 3D reconstruction. Besides, we aimed at exploring the computation ability of computer and making SfM easily transferred to distributed system. This work has been reconstructed and now it is based on COLMAP.
In our work, 3D reconstruction is deemed as a divide-and-conquer problem. Our graph cluster algorithm divides images into different clusters, while images with high relativity remained in the same group. After the completion of local SfM in all clusters, an elaborate graph initialization and MST construction algorithm is designed to accurately merge clusters, and cope well with drift problems. The two proposed graph-based algorithms make SfM more efficient and robust - the graph cluster algorithm accelerate the SfM step while guarantee the robustness of clusters merging, and the MST construction makes point clouds alignment as accurate as possible. Our approach can reconstruct large scale data-set in one single machine with very high accuracy and efficiency.
If you use this project for your research, please cite:
@misc{chen2019graphbased,
title={Graph-Based Parallel Large Scale Structure from Motion},
author={Yu Chen and Shuhan Shen and Yisong Chen and Guoping Wang},
year={2019},
eprint={1912.10659},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{schoenberger2016sfm,
author={Sch\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael},
title={Structure-from-Motion Revisited},
booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2016},
}
sudo apt-get install \
git \
cmake \
build-essential \
libboost-program-options-dev \
libboost-filesystem-dev \
libboost-graph-dev \
libboost-regex-dev \
libboost-system-dev \
libboost-test-dev \
libeigen3-dev \
libsuitesparse-dev \
libfreeimage-dev \
libgoogle-glog-dev \
libgflags-dev \
libglew-dev \
qtbase5-dev \
libqt5opengl5-dev \
libcgal-dev \
libcgal-qt5-dev
Then install ceres-solver
sudo apt-get install libatlas-base-dev libsuitesparse-dev
git clone https://ceres-solver.googlesource.com/ceres-solver
cd ceres-solver
git checkout $(git describe --tags) # Checkout the latest release
mkdir build
cd build
cmake .. -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF
make
sudo make installBuild our GraphSfM
git clone https://github.com/AIBluefisher/GraphSfM.git
cd GraphSfM
mkdir build
cd build
cmake ..
make -j8
sudo make installAs our algorithm is not integrated in the GUI of COLMAP, we offer a script to run the
distributed SfM (We hope there is anyone that is interested in integrating this pipeline into the GUI):
sudo chmod +x scripts/shell/distributed_sfm.sh
./distributed_sfm.sh $image_dir $num_images_ub $log_folder $completeness_ratio$image_dir: The directory that stores images$num_images_ub: The maximum image number in each cluster. For example,80~120.$log_folder: The directory that stores the logs$completeness_ratio: The ratio that measure the repeatitive rate of adjacent clusters.
If succeed, camera poses and sparse points should be included in $DATASET/sparse folder, you can use COLMAP's GUI to
import it and show the visual result:
./build/src/exe/colmap guiFor small scale reconstruction, you can set the $num_images_ub equal to the number of images, the program would just use the incremental SfM pipeline of COLMAP.
For large scale reconstruction, our GraphSfM is highly recommended, these parameters should be tuned carefully: larger $num_images_ub and $completeness_ratio can make reconstruction more robust, but also may lead to low efficiency and even degenerate to incremental one.
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