qinziwen / graphsfm

Robust and Efficient Graph-based Structure from Motion

Graph Structure from Motion (GraphSfM)

A similar version of GraphSfM based on OpenMVG has been released in: https://github.com/AIBluefisher/EGSfM.

1. Overview of GraphSfM

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 refactored, now it is based on COLMAP. We have implemented the distributed version which is based on Map-Reduce architecture.

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. Based on this work, we have been successfully reconstructed Peking University within 2 hours on a laptop.

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},
}

2. How to Build

2.1 Required

Basic Requirements

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

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 install

rpclib

git clone https://github.com/qchateau/rpclib.git
cd rpclib
mkdir build && cd build
cmake ..
make -j8
sudo make install

Build our GraphSfM

git clone https://github.com/AIBluefisher/GraphSfM.git
cd GraphSfM
mkdir build && cd build
cmake .. && make -j8

3. Usage

As 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).

Sequential Mode

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.

Distributed Mode

(1) At first, we need to establish the server for every worker:

cd build/src/exe
./colmap local_sfm_worker --output_path=$output_path

(2) Then, the ip and port for every server should be written in a config.txt file. The file format should follow:

server_num
ip1 port1
ip2 port2
... ...

(3) At last, start our master

# The project folder must contain a folder "images" with all the images.
DATASET_PATH=/path/to/project
CONFIG_FILE_PATH=/path/to/config.txt

./colmap distributed_mapper $DATASET_PATH/Master \
                            --database_path=$DATASET_PATH/database.db \
                            --image_path=$DATASET_PATH/images \
                            --output_path=$DATASET_PATH/Master \
                            --distributed=1 \
                            --config_file_name=$CONFIG_FILE_PATH \ 
                            --num_images_ub=$num_images_ub \
                            --graph_dir=$output_path \
                            --cluster_type=NCUT \
                            --num_workers=8

If succeed, camera poses and sparse points should be included in $DATASET/Master folder, you can use COLMAP's GUI to import it and show the visual result:

./build/src/exe/colmap gui

For 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.

ChangeLog

• 2020.03.06

  • map reduce implementation for distributed Structure-from-Motion.

• 2019.11.26

  • Image Clustering algorithms: NCut, Spectral Clustering (Upcoming)
  • Graph-based sub-reconstruction Merging algorithm

Licence

BSD 3-Clause License

Copyright (c) 2018, 陈煜
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

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