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)

In our work, 3D reconstruction is deemed as a

divide-and-conquer

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

COLMAP

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:

sh
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

txt
server_num
ip1 port1 image_path1
ip2 port2 image_path2
... ...

(3) At last, start our master ```sh cd GraphSfM_PATH/scripts/shell

The project folder must contain a folder "images" with all the images.

DATASETPATH=/path/to/project CONFIGFILEPATH=/pathtoconfigfile numimagesub=100 logfolder=/pathtologdir

./distributedsfm sh $DATASETPATH $numimagesub $logfolder $CONFIGFILE_PATH ```

The

distributed_sfm.sh

sh
/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

--transfer_images_to_server

1

• --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

sh
./build/src/exe/colmap gui

$num_images_ub

For large scale reconstruction, our

GraphSfM

$num_images_ub

$completeness_ratio

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

sh
./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

images.txt

cameras.txt

points3D.txt

images.bin

cameras.bin

points3D.bin

max_image_num

• 'write_binary

  : set to

  1

  if save colmap data in binary format, or set to

  0` 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
All rights reserved.
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