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 (contains 12306 images) within 2 hours on a laptop, and Trafalgar dataset (contains more than 15000 images, we render the camera poses in different clusters with different colors).

If you use this project for your research, please cite:

@article{article,
author = {Chen, Yu and Shen, Shuhan and Chen, Yisong and Wang, Guoping},
year = {2020},
month = {07},
pages = {107537},
title = {Graph-Based Parallel Large Scale Structure from Motion},
journal = {Pattern Recognition},
doi = {10.1016/j.patcog.2020.107537}
}

@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 image_path1
ip2 port2 image_path2
... ...

(3) At last, start our master

cd GraphSfM_PATH/scripts/shell
# The project folder must contain a folder "images" with all the images.
DATASET_PATH=/path/to/project
CONFIG_FILE_PATH=/path_to_config_file
num_images_ub=100
log_folder=/path_to_log_dir

./distributed_sfm sh $DATASET_PATH $num_images_ub $log_folder $CONFIG_FILE_PATH

The distributed_sfm.sh actually executes the following command in SfM module:

/home/chenyu/Projects/Disco/build/src/exe/colmap distributed_mapper \
$DATASET_PATH/$log_folder \
--database_path=$DATASET_PATH/database.db \
--transfer_images_to_server=1 \
--image_path=$DATASET_PATH/images \
--output_path=$DATASET_PATH/$log_folder \
--config_file_name=$CONFIG_FILE_PATH/config.txt \
--num_workers=8 \
--distributed=1 \
--repartition=0 \
--assign_cluster_id=1 \
--write_binary=1 \
--retriangulate=0 \
--final_ba=1 \
--select_tracks_for_bundle_adjustment=1 \
--long_track_length_threshold=10 \
--graph_dir=$DATASET_PATH/$log_folder \
--num_images_ub=$num_images_ub \
--completeness_ratio=0.7 \
--relax_ratio=1.3 \
--cluster_type=SPECTRA

The parameters need to be reset for different purpose:

• --transfer_images_to_server: The option decides whether to transfer images that are stored on master's disk to workers' disks. If we want to execute a further MVS process, we want this option to be set to 1, because each worker that execute MVS needs to access the raw images.

• --distributed: This option decides the SfM module runs in distributed mode or sequential mode. For example, if we just have one computer, we should set it to 0, then SfM would run in sequential mode and allows you to reconstruct large scale images on a single computer. If we set it to 1, we must ensure the --config_file_name option is valid, so that we we run SfM among a lot of computers, which in a really distributed mode.

• assign_cluster_id: We use this option to indicate the program to assign each image with a cluster_id, if we divide images into several clusters. This option allows us to render different image poses that are clustered in different clusters by different colors.

• write_binary: This option indicates to save the SfM results in text format or in binary format.

• final_ba: This option indicates whether to perform a final bundle adjustment after merging all local maps. As a very large scale map requires much time to optimize scene structures and camera poses, users should tune this option by their need.

• select_tracks_for_bundle_adjustment: As the final bundle adjustment requires too much time, we can select good tracks to optimize and achieves a comparable accuracy as full bundle adjustment.

• long_track_length_threshold: The maximum track length when selects good tracks for bundle adjustment.

• num_images_ub: The maximum number of images in each cluster.

• completeness_ratio: This option indicate the overlapping between clusters. 0.5~0.7 is enough in practice.

• cluster_type: This option decides which cluster method we choose for image clustering. We support NCut and Spectral Clustering. Spectra clustering is more accurate than NCut but it would be slow if we want to divide images into many clusters, as it needs much time to compute eigen vectors.

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.

Segment large scale maps

In some cases where we have a very large scale map, such that a latter Multi-View Stereo becomes infeasible because of memory limitation. We can use the point_cloud_segmenter to segment original map that is stored in colmap format into multiple small maps.

./build/src/exe/colmap point_cloud_segmenter \
--colmap_data_path=path_to_colmap_data \ 
--output_path=path_to_store_small_maps \
--max_image_num=max_number_image_for_small_map \
--write_binary=1

Before running this command, make sure path_to_colmap_data contains images.txt, cameras.txt, points3D.txt or images.bin, cameras.bin, points3D.bin.

• max_image_num: As colmap data includes images data, where store all registered images' data. We limit the image number of each small map, and use this parameter to segment large maps. Though it's better to use the number of point clouds in practice, we haven't release the related implementation and we will enhance this helper further.

• 'write_binary: set to 1if save colmap data in binary format, or set to0` to save colmap data in text format.

ChangeLog

• 2020.04.11

  • Interface for extracting largest connected component in graph implementation.
  • Merge largest connected component in SfMAligner.
  • Command line helper for merging multiple local maps.
  • Command line helper for segmenting large scale map into several sub-maps.

• 2020.04.10

  • Spectral clustering for image clustering.
  • Select good tracks for final bundle adjustment.
  • Image transfer from master to workers.
  • Extract largest connected component for Structure-from-Motion.

• 2020.03.06

  • Assign different colors for images in different clusters.
  • Support rendering image poses by different colors in ui.
  • map reduce implementation for distributed Structure-from-Motion.

• 2019.11.26

  • Image Clustering algorithms: NCut, Spectral Clustering (Updated in latest version).
  • Graph-based sub-reconstruction Merging algorithm.

Licence

BSD 3-Clause License

Copyright (c) 2018, 陈煜
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