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1. 介绍

照相机是将一个三维场景或物体投影到二维平面上，降维的过程通常会存在信息的损失，而重建(Reconstruction)就是要从获取到的众多二维图像中复原原始三维场景或物体。具体流程就是：

• 通过多角度拍摄或者从视频中提取得到一组图像序列，将这些图像序列作为整个系统的输入
• 在多视角的图像中，根据纹理特征提取出稀疏特征点（称为点云），通过这些特征点估计相机位置和参数
• 得到相机参数并完成特征点匹配后，就可以获得更稠密的点云
• 根据这些点重建物体表面，并进行纹理映射，就还原出三维场景和物体了

简略来说就是：图像获取->特征匹配->深度估计->稀疏点云->相机参数估计->稠密点云->表面重建->纹理映射

2. 下载OpenSfm

2.1 下载opensfm的原始github库

• 访问OpenSfm的项目主页查看安装步骤：
  git clone --recursive https://github.com/mapillary/OpenSfM
  如果速度慢，可以使用git config --global https.https://github.com.proxy socks5://127.0.0.1:1080
  注意，递归方式才会下载OpenSfM/opensfm/src/third_party/pybind11文件夹下的内容，否则要自己下载pybind11的zip文件解压在对应位置：
  rmdir pybind11/ && git clone https://github.com/pybind/pybind11.git
• 也可以opensfm下载release版本0.5.1，然后解压进入pybind11文件夹下载pybind11的zip文件

注意
最好选择：OpenSfM v0.5.1、pybind/pybind11 v2.2.4

2.2 安装依赖

使用如下命令安装依赖：

sudo apt-get install build-essential cmake libatlas-base-dev libatlas-base-dev libgoogle-glog-dev \
                     libopencv-dev libsuitesparse-dev python3-pip python3-dev  python3-numpy python3-opencv \
                     python3-pyproj python3-scipy python3-yaml libeigen3-dev

安装opengv，官网教程，具体步骤如下（DPYTHON_INSTALL_DIR是要安装到的目录）：

mkdir source && cd source/
git clone --recurse-submodules -j8 https://github.com/laurentkneip/opengv.git
cd opengv && mkdir build && cd build
cmake .. -DBUILD_TESTS=OFF -DBUILD_PYTHON=ON -DPYBIND11_PYTHON_VERSION=3.6 -DPYTHON_INSTALL_DIR=/usr/local/lib/python3.6/dist-packages/
sudo make install

安装ceres，可以按照此步骤

cd ../../
curl -L http://ceres-solver.org/ceres-solver-1.14.0.tar.gz | tar xz
cd ./ceres-solver-1.14.0 && mkdir build-code && cd build-code
cmake .. -DCMAKE_C_FLAGS=-fPIC -DCMAKE_CXX_FLAGS=-fPIC -DBUILD_EXAMPLES=OFF -DBUILD_TESTING=OFF
sudo make -j4 install

安装pip库，然后build这个opensfm的库，安装在pip里面

cd ../../../ && pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
python3 setup.py build

此时opensfm即安装成功

3. 测试

在opensfm主目录下

bin/opensfm_run_all data/berlin
python3 -m http.server

点击viewer文件夹，选择reconstruction.html打开，然后选择上面命令生成的文件data/berlin/reconstruction.meshed.json；也可以在undistorted文件夹下面找到merged.ply文件打开即可
如果使用SIFT提取特征，需要pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple opencv-contrib-python==3.4.2.16（opencv-python版本不用改动）

4. 注意事项

如果PATH环境变量设置的是某个python虚拟环境优先（即运行which python3看到某个虚拟环境的路径），同时又想把opensfm配置到系统python里面：严格按照官网安装链接，只是把其中的python3换成/usr/bin/python3，pip3换成/usr/bin/pip3（如果本机的PATH修改过），即

# 完整下载OpenSfM仓库（2317fbb），包括里面的pybind11等
git clone --recursive https://github.com/mapillary/OpenSfM opensfm
# 进入opensfm主目录
cd opensfm
# 再次更新子模块保证最新
git submodule update --init --recursive
# 更新源
sudo apt-get update
# 安装依赖的包
sudo apt-get install -y \
    build-essential vim curl cmake git \
    libatlas-base-dev libeigen3-dev \
    libgoogle-glog-dev libopencv-dev libsuitesparse-dev \
    python3-dev python3-numpy python3-opencv python3-pip \
    python3-pyproj python3-scipy python3-yaml
# ---------编译安装ceres---------
# 创建临时目录
mkdir source && cd source
# 下载ceres v1.14并解压
curl -L http://ceres-solver.org/ceres-solver-1.14.0.tar.gz | tar xz
# 创建编译文件夹
cd ceres-solver-1.14.0 && mkdir build && cd build
# cmake
cmake .. -DCMAKE_C_FLAGS=-fPIC -DCMAKE_CXX_FLAGS=-fPIC -DBUILD_EXAMPLES=OFF -DBUILD_TESTING=OFF
# 开启48线程编译安装
sudo make -j48 install
# ----------编译安装opengv-------
# 回到source文件夹下
cd ../../
# 下载opengv
git clone https://github.com/paulinus/opengv.git
# 更新子模块保证代码最新
cd opengv && git submodule update --init --recursive
# 创建编译文件夹
mkdir build && cd build
# cmake
cmake .. -DBUILD_TESTS=OFF \
         -DBUILD_PYTHON=ON \
         -DPYBIND11_PYTHON_VERSION=3.6 \
         -DPYTHON_INSTALL_DIR=/usr/local/lib/python3.6/dist-packages/
# 开启48线程编译安装
sudo make -j48 install
# 安装opensfm需要的python库
/usr/bin/pip3 install \
    exifread==2.1.2 gpxpy==1.1.2 networkx==1.11 \
    numpy pyproj==1.9.5.1 pytest==3.0.7 \
    python-dateutil==2.6.0 PyYAML==3.12 \
    scipy xmltodict==0.10.2 \
    loky repoze.lru
# ----------编译opensfm----------
/usr/bin/python3 setup.py build
# 安装特定版本的opencv-contrib，此时可用SIFT特征提取算法
/usr/bin/pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple opencv-contrib-python==3.4.2.16

安装以后，使用时首先export PATH=，把/usr/bin放在第一位，保证python3调用的是/usr/bin/python3

5. 配置文件

每次运行opensfm生成点云，不仅需要原始图片数据，还需要一个配置文件config.yaml，文件结构如下：

lab
├── config.yaml
└── images
    ├── DJI_1_0239.JPG
    ├── DJI_1_0240.JPG
    ├── DJI_1_0242.JPG
    └── DJI_1_0268.JPG

1 directory, 5 files

配置文件的默认选项如下，见链接opensfm.org

# Metadata
use_exif_size: yes
default_focal_prior: 0.85

# Params for features
feature_type: HAHOG           # Feature type (AKAZE, SURF, SIFT, HAHOG, ORB)
feature_root: 1               # If 1, apply square root mapping to features
feature_min_frames: 4000      # If fewer frames are detected, sift_peak_threshold/surf_hessian_threshold is reduced.
feature_process_size: 2048    # Resize the image if its size is larger than specified. Set to -1 for original size
feature_use_adaptive_suppression: no

# Params for SIFT
sift_peak_threshold: 0.1     # Smaller value -> more features
sift_edge_threshold: 10       # See OpenCV doc

# Params for SURF
surf_hessian_threshold: 3000  # Smaller value -> more features
surf_n_octaves: 4             # See OpenCV doc
surf_n_octavelayers: 2        # See OpenCV doc
surf_upright: 0               # See OpenCV doc

# Params for AKAZE (See details in lib/src/third_party/akaze/AKAZEConfig.h)
akaze_omax: 4                      # Maximum octave evolution of the image 2^sigma (coarsest scale sigma units)
akaze_dthreshold: 0.001            # Detector response threshold to accept point
akaze_descriptor: MSURF            # Feature type
akaze_descriptor_size: 0           # Size of the descriptor in bits. 0->Full size
akaze_descriptor_channels: 3       # Number of feature channels (1,2,3)
akaze_kcontrast_percentile: 0.7
akaze_use_isotropic_diffusion: no

# Params for HAHOG
hahog_peak_threshold: 0.00001
hahog_edge_threshold: 10
hahog_normalize_to_uchar: yes

# Params for general matching
lowes_ratio: 0.8              # Ratio test for matches
matcher_type: FLANN           # FLANN, BRUTEFORCE, or WORDS
symmetric_matching: yes       # Match symmetricly or one-way

# Params for FLANN matching
flann_branching: 8           # See OpenCV doc
flann_iterations: 10          # See OpenCV doc
flann_checks: 20             # Smaller -> Faster (but might lose good matches)

# Params for BoW matching
bow_file: bow_hahog_root_uchar_10000.npz
bow_words_to_match: 50        # Number of words to explore per feature.
bow_num_checks: 20            # Number of matching features to check.
bow_matcher_type: FLANN       # Matcher type to assign words to features

# Params for VLAD matching
vlad_file: bow_hahog_root_uchar_64.npz

# Params for matching
matching_gps_distance: 150            # Maximum gps distance between two images for matching
matching_gps_neighbors: 0             # Number of images to match selected by GPS distance. Set to 0 to use no limit (or disable if matching_gps_distance is also 0)
matching_time_neighbors: 0            # Number of images to match selected by time taken. Set to 0 to disable
matching_order_neighbors: 0           # Number of images to match selected by image name. Set to 0 to disable
matching_bow_neighbors: 0             # Number of images to match selected by BoW distance. Set to 0 to disable
matching_bow_gps_distance: 0          # Maximum GPS distance for preempting images before using selection by BoW distance. Set to 0 to disable
matching_bow_gps_neighbors: 0         # Number of images (selected by GPS distance) to preempt before using selection by BoW distance. Set to 0 to use no limit (or disable if matching_bow_gps_distance is also 0)
matching_bow_other_cameras: False     # If True, BoW image selection will use N neighbors from the same camera + N neighbors from any different camera.
matching_vlad_neighbors: 0            # Number of images to match selected by VLAD distance. Set to 0 to disable
matching_vlad_gps_distance: 0         # Maximum GPS distance for preempting images before using selection by VLAD distance. Set to 0 to disable
matching_vlad_gps_neighbors: 0        # Number of images (selected by GPS distance) to preempt before using selection by VLAD distance. Set to 0 to use no limit (or disable if matching_vlad_gps_distance is also 0)
matching_vlad_other_cameras: False    # If True, VLAD image selection will use N neighbors from the same camera + N neighbors from any different camera.
matching_use_filters: False           # If True, removes static matches using ad-hoc heuristics

# Params for geometric estimation
robust_matching_threshold: 0.004        # Outlier threshold for fundamental matrix estimation as portion of image width
robust_matching_calib_threshold: 0.004  # Outlier threshold for essential matrix estimation during matching in radians
robust_matching_min_match: 20           # Minimum number of matches to accept matches between two images
five_point_algo_threshold: 0.004        # Outlier threshold for essential matrix estimation during incremental reconstruction in radians
five_point_algo_min_inliers: 20         # Minimum number of inliers for considering a two view reconstruction valid
five_point_refine_match_iterations: 10  # Number of LM iterations to run when refining relative pose during matching
five_point_refine_rec_iterations: 1000  # Number of LM iterations to run when refining relative pose during reconstruction
triangulation_threshold: 0.006          # Outlier threshold for accepting a triangulated point in radians
triangulation_min_ray_angle: 1.0        # Minimum angle between views to accept a triangulated point
triangulation_type: FULL                # Triangulation type : either considering all rays (FULL), or sing a RANSAC variant (ROBUST)
resection_threshold: 0.004              # Outlier threshold for resection in radians
resection_min_inliers: 10               # Minimum number of resection inliers to accept it

# Params for track creation
min_track_length: 2             # Minimum number of features/images per track

# Params for bundle adjustment
loss_function: SoftLOneLoss     # Loss function for the ceres problem (see: http://ceres-solver.org/modeling.html#lossfunction)
loss_function_threshold: 1      # Threshold on the squared residuals.  Usually cost is quadratic for smaller residuals and sub-quadratic above.
reprojection_error_sd: 0.004    # The standard deviation of the reprojection error
exif_focal_sd: 0.01             # The standard deviation of the exif focal length in log-scale
principal_point_sd: 0.01        # The standard deviation of the principal point coordinates
radial_distorsion_k1_sd: 0.01   # The standard deviation of the first radial distortion parameter
radial_distorsion_k2_sd: 0.01   # The standard deviation of the second radial distortion parameter
radial_distorsion_k3_sd: 0.01   # The standard deviation of the third radial distortion parameter
radial_distorsion_p1_sd: 0.01   # The standard deviation of the first tangential distortion parameter
radial_distorsion_p2_sd: 0.01   # The standard deviation of the second tangential distortion parameter
bundle_outlier_filtering_type: FIXED    # Type of threshold for filtering outlier : either fixed value (FIXED) or based on actual distribution (AUTO)
bundle_outlier_auto_ratio: 3.0          # For AUTO filtering type, projections with larger reprojection than ratio-times-mean, are removed
bundle_outlier_fixed_threshold: 0.006   # For FIXED filtering type, projections with larger reprojection error after bundle adjustment are removed
optimize_camera_parameters: yes         # Optimize internal camera parameters during bundle
bundle_max_iterations: 100      # Maximum optimizer iterations.

retriangulation: yes                # Retriangulate all points from time to time
retriangulation_ratio: 1.2          # Retriangulate when the number of points grows by this ratio
bundle_interval: 999999             # Bundle after adding 'bundle_interval' cameras
bundle_new_points_ratio: 1.2        # Bundle when the number of points grows by this ratio
local_bundle_radius: 3              # Max image graph distance for images to be included in local bundle adjustment
local_bundle_min_common_points: 20  # Minimum number of common points betwenn images to be considered neighbors
local_bundle_max_shots: 30          # Max number of shots to optimize during local bundle adjustment

save_partial_reconstructions: no    # Save reconstructions at every iteration

# Params for GPS alignment
use_altitude_tag: no                  # Use or ignore EXIF altitude tag
align_method: orientation_prior       # orientation_prior or naive
align_orientation_prior: horizontal   # horizontal, vertical or no_roll
bundle_use_gps: yes                   # Enforce GPS position in bundle adjustment
bundle_use_gcp: no                    # Enforce Ground Control Point position in bundle adjustment

# Params for navigation graph
nav_min_distance: 0.01                # Minimum distance for a possible edge between two nodes
nav_step_pref_distance: 6             # Preferred distance between camera centers
nav_step_max_distance: 20             # Maximum distance for a possible step edge between two nodes
nav_turn_max_distance: 15             # Maximum distance for a possible turn edge between two nodes
nav_step_forward_view_threshold: 15   # Maximum difference of angles in degrees between viewing directions for forward steps
nav_step_view_threshold: 30           # Maximum difference of angles in degrees between viewing directions for other steps
nav_step_drift_threshold: 36          # Maximum motion drift with respect to step directions for steps in degrees
nav_turn_view_threshold: 40           # Maximum difference of angles in degrees with respect to turn directions
nav_vertical_threshold: 20            # Maximum vertical angle difference in motion and viewing direction in degrees
nav_rotation_threshold: 30            # Maximum general rotation in degrees between cameras for steps

# Params for image undistortion
undistorted_image_format: jpg         # Format in which to save the undistorted images
undistorted_image_max_size: 100000    # Max width and height of the undistorted image

# Params for depth estimation
depthmap_method: PATCH_MATCH_SAMPLE   # Raw depthmap computation algorithm (PATCH_MATCH, BRUTE_FORCE, PATCH_MATCH_SAMPLE)
depthmap_resolution: 640              # Resolution of the depth maps
depthmap_num_neighbors: 10            # Number of neighboring views
depthmap_num_matching_views: 6        # Number of neighboring views used for each depthmaps
depthmap_min_depth: 0                 # Minimum depth in meters.  Set to 0 to auto-infer from the reconstruction.
depthmap_max_depth: 0                 # Maximum depth in meters.  Set to 0 to auto-infer from the reconstruction.
depthmap_patchmatch_iterations: 3     # Number of PatchMatch iterations to run
depthmap_patch_size: 7                # Size of the correlation patch
depthmap_min_patch_sd: 1.0            # Patches with lower standard deviation are ignored
depthmap_min_correlation_score: 0.1   # Minimum correlation score to accept a depth value
depthmap_same_depth_threshold: 0.01   # Threshold to measure depth closeness
depthmap_min_consistent_views: 3      # Min number of views that should reconstruct a point for it to be valid
depthmap_save_debug_files: no         # Save debug files with partial reconstruction results

# Other params
processes: 1                  # Number of threads to use

# Params for submodel split and merge
submodel_size: 80                                                    # Average number of images per submodel
submodel_overlap: 30.0                                               # Radius of the overlapping region between submodels
submodels_relpath: "submodels"                                       # Relative path to the submodels directory
submodel_relpath_template: "submodels/submodel_%04d"                 # Template to generate the relative path to a submodel directory
submodel_images_relpath_template: "submodels/submodel_%04d/images"   # Template to generate the relative path to a submodel images directory