[转]一篇文章教你学会使用三维重建知名开源系统

git clone https://github.com/snavely/bundler_sfm.git
git clone https://github.com/pmoulon/CMVS-PMVS.git

sudo apt-get install liblapack-dev libblas-dev minpack-dev f2c gfortran jhead imagemagick

sudo apt-get install libc6-dev-i386

sudo apt-get install libgtk2.0-dev libdevil-dev libboost-all-dev libatlas-cpp-0.6-dev libatlas-dev libcminpack-dev libgfortran3 libmetis-edf-dev libparmetis-dev freeglut3-dev libgsl0-dev

　sudo cp /usr/bin/jhead bin/　

　sudo cp bin/libANN_char.so /usr/lib/　

 mkdir build && cd build
 cmake ..
 make

../../RunBundler.sh

../../bin/Bundle2PMVS prepare/list.txt bundle/bundle.out

sh pmvs/prep_pmvs.sh
../../bin/cmvs pmvs/
../../bin/genOption pmvs/
../../bin/pmvs2 pmvs/ option-0000 (注意option之前的空格键)

系统：ubuntu17.10
编译器：gcc 7.2.0

<span style="font-size:18px;">git clone https://github.com/simonfuhrmann/mve.git</span>

<span style="font-size:18px;">cd mve</span>

<span style="font-size:18px;">make -j8</span>

<span style="font-size:18px;">vi ~/.bashrc</span>

<span style="font-size:18px;">source ~/.bashrc</span>

<span style="font-size:18px;">makescene -i der_hass-20140923 der_hass</span>

<span style="font-size:18px;">sfmrecon der_hass
dmrecon -s2 der_hass
scene2pset -F2 der_hass der_hass/pset-L2.ply
fssrecon der_hass/pset-L2.ply der_hass/surface-L2.ply
meshclean -t10 der_hass/surface-L2.ply der_hass/surface-L2-clean.ply</span>

#准备一个空闲的机器安装:
sudo apt-get update -qq && sudo apt-get install -qq 
sudo apt-get -y install git cmake libpng-dev libjpeg-dev libtiff-dev libglu1-mesa-dev 
 
#安装Eigen (必需) 
git clone https://gitlab.com/libeigen/eigen.git --branch 3.2 
mkdir eigen_build && cd eigen_build 
cmake . ../eigen make && sudo make install 
cd .. 


#安装Boost (必需) 
sudo apt-get -y install libboost-iostreams-dev libboost-program-options-dev libboost-system-dev libboost-serialization-dev  

#安装OpenCV (必需) 
sudo apt-get -y install libopencv-dev  


#安装CGAL (必需) 
sudo apt-get -y install libcgal-dev libcgal-qt5-dev  


#安装VCGLib (必需) 
git clone https://github.com/cdcseacave/VCG.git vcglib  


#安装Ceres (可选) 
sudo apt-get -y install libatlas-base-dev libsuitesparse-dev 
git clone https://ceres-solver.googlesource.com/ceres-solver ceres-solver 
mkdir ceres_build && cd ceres_build 
cmake . ../ceres-solver/ -DMINIGLOG=ON -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF 
make -j2 && sudo make install 
cd ..  


#安装GLFW3 (可选) 
sudo apt-get -y install freeglut3-dev libglew-dev libglfw3-dev  


#安装OpenMVS 
git clone https://github.com/electech6/openMVS_comments.git openMVS 
mkdir openMVS_build && cd openMVS_build 
cmake . ../openMVS -DCMAKE_BUILD_TYPE=Release -DVCG_ROOT="/path to vcglib/vcglib"  


#如果想生成共享库,可以在cmake加如下命令: 
#-DBUILD_SHARED_LIBS=ON  


#生成 OpenMVS 库文件: 
sudo make -j2 && sudo make install 

sudo apt-get autoremove opencv-doc opencv-data libopencv-dev libopencv2.4-java libopencv2.4-jni python-opencv libopencv-core2.4 libopencv-gpu2.4 libopencv-ts2.4 libopencv-photo2.4 libopencv-contrib2.4 libopencv-imgproc2.4 libopencv-superres2.4 libopencv-stitching2.4 libopencv-ocl2.4 libopencv-legacy2.4 libopencv-ml2.4 libopencv-video2.4 libopencv-videostab2.4 libopencv-objdetect2.4 libopencv-calib3d2.4 

./bin/DensifyPointCloud -w /home/**/data/openMVS_sample -i scene.mvs -o test_dense.mvs 

./bin/ReconstructMesh -w /home/data -i test_dense.mvs -o test_mesh.mvs 

./bin/RefineMesh -w /home/data -i test_mesh.mvs -o test_refinemesh.mvs 

./bin/TextureMesh -w /home/data -i test_refinemesh.mvs -o test_texture.mvs 

# 安装依赖 sudo apt-get install libpng-dev libjpeg-dev libtiff-dev libxxf86vm1 libxxf86vm-dev libxi-dev libxrandr-dev graphviz  
# 克隆代码 git clone --recursive https://github.com/openMVG/openMVG.git  
# configure && build mkdir openMVG_Build &&cd openMVG_Build cmake -DCMAKE_BUILD_TYPE=RELEASE ../openMVG/src/   -DOpenMVG_BUILD_TESTS=ON sudo cmake --build . --target install  
# test make test ctest --output-on-failure -j 
# .bashrcexport PATH=$PATH:/home/work/tools/openMVG_Build/Linux-x86_64-RELEASE/

git clone https://github.com/pmoulon/CMVS-PMVS.git 
cd CMVS-PMVS 
mkdir build && cd build 
cmake ../program/ 
make 
sudo cp main/pmvs2 main/genOption main/cmvs /usr/local/bin/

cd openMVG 
vim 3dr_test.py  
#!/usr/bin/python
#! -*- encoding: utf-8 -*-
# openmvg使用示例
# usage : python tutorial_demo.py  
import os 
import subprocess 
import sys  # openmvg编译bin目录(可cp -p到/usr/local/bin/) 
OPENMVG_SFM_BIN = "/home/work/tools/openMVG_Build/Linux-x86_64-RELEASE"
# pmvs编译bin目录(可cp -p到/usr/local/bin/) PMVS_BIN = "/home/work/tools/CMVS-PMVS/build/main"
# openmvg相机参数目录
CAMERA_SENSOR_WIDTH_DIRECTORY = "/home/work/tools/openMVG/src/openMVG/exif/sensor_width_database"
# 0. 下载测试照片 
os.chdir(os.path.dirname(os.path.abspath(__file__))) 
data_dir = os.path.abspath("./book") 
#data_dir = os.path.abspath("./ImageDataset_SceauxCastle")
'''if not os.path.exists(data_dir):   
     pImageDataCheckout = subprocess.Popen([ "git", "clone","https://github.com/openMVG/ImageDataset_SceauxCastle.git" ])   
     pImageDataCheckout.wait()''' 
input_dir = os.path.join(data_dir, "images") 
output_dir = data_dir 
print ("Using input dir  : ", input_dir) 
print ("      output_dir : ", output_dir) 
matches_dir = os.path.join(output_dir, "matches") 
camera_file_params = os.path.join(CAMERA_SENSOR_WIDTH_DIRECTORY, "sensor_width_camera_database.txt")    #相机参数
if not os.path.exists(matches_dir):   
       os.mkdir(matches_dir)  

# 1. 从图片数据集中生成场景描述文件sfm_data.json 
print ("----------1. Intrinsics analysis----------") 
pIntrisics = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_SfMInit_ImageListing"),  "-i", input_dir, "-o", matches_dir, "-d", camera_file_params, "-c", "3"] ) 
#*注：如果产出的sfm_data.json里intrinsics内容为空，通常是在图片没有exif信息导致获取不到相机焦距、ccd尺寸等参数，用带exif的原图即可。 
pIntrisics.wait()  

# 2. 计算图像特征 
print ("----------2. Compute features----------") 
pFeatures = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeFeatures"),  "-i", matches_dir+"/sfm_data.json", "-o", matches_dir, "-m", "SIFT", "-f" , "1"] ) 
pFeatures.wait()  

# 3. 计算几何匹配 
print ("----------3. Compute matches----------") 
pMatches = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeMatches"),  "-i", matches_dir+"/sfm_data.json", "-o", matches_dir, "-f", "1", "-n", "ANNL2"] ) 
pMatches.wait()  

# 4. 执行增量三维重建 
reconstruction_dir = os.path.join(output_dir,"reconstruction_sequential") 
print ("----------4. Do Incremental/Sequential reconstruction----------") #set manually the initial pair to avoid the prompt question 
pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_IncrementalSfM"),  "-i", matches_dir+"/sfm_data.json", "-m", matches_dir, "-o", reconstruction_dir] ) 
pRecons.wait()  

# 5. 计算场景结构颜色 
print ("----------5. Colorize Structure----------") 
pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeSfM_DataColor"),  "-i", reconstruction_dir+"/sfm_data.bin", "-o", os.path.join(reconstruction_dir,"colorized.ply")] ) 
pRecons.wait()  
# 6. 测量稳健三角 
print ("----------6. Structure from Known Poses (robust triangulation)----------") 
pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeStructureFromKnownPoses"),  "-i", reconstruction_dir+"/sfm_data.bin", "-m", matches_dir, "-o", os.path.join(reconstruction_dir,"robust.ply")] ) 
pRecons.wait()  
'''
# 使用全局SfM管道重建
Reconstruction for the global SfM pipeline
# 3.1 全局sfm管道几何匹配 
print ("----------3.1. Compute matches (for the global SfM Pipeline)----------") 
pMatches = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeMatches"),  "-i", matches_dir+"/sfm_data.json", "-o", matches_dir, "-r", "0.8", "-g", "e"] ) 
pMatches.wait()  
# 4.1 执行全局三维重建
 reconstruction_dir = os.path.join(output_dir,"reconstruction_global") 
print ("----------4.1. Do Global reconstruction----------") 
pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_GlobalSfM"),  "-i", matches_dir+"/sfm_data.json", "-m", matches_dir, "-o", reconstruction_dir] )
pRecons.wait()  
# 5.1 计算场景结构颜色 
print ("----------5.1. Colorize Structure----------") 
pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeSfM_DataColor"),  "-i", reconstruction_dir+"/sfm_data.bin", "-o", os.path.join(reconstruction_dir,"colorized.ply")] ) 
pRecons.wait()  
# 6.1 测量稳健三角 
print ("----------6.1. Structure from Known Poses (robust triangulation)----------") 
pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeStructureFromKnownPoses"),  "-i", reconstruction_dir+"/sfm_data.bin", "-m", matches_dir, "-o", os.path.join(reconstruction_dir,"robust.ply")] ) 
pRecons.wait()
 '''
# 7. 把openMVG生成的SfM_Data转为适用于PMVS输入格式的文件 print ("----------7. Export to PMVS/CMVS----------") 
pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_openMVG2PMVS"),  "-i", reconstruction_dir+"/sfm_data.bin", "-o", reconstruction_dir] ) 
pRecons.wait() 
#*注：执行后会在-o路径下生成一个PMVS目录，包含 models, txt, visualize 三个子目录：models为空；txt包含对应图像的txt文档，每个里面都是一个3x4的矩阵，大概是相机位姿；visualize包含11张图像，不确定是原图像还是校正过的图像
# 8. 使用PMVS重建稠密点云、表面、纹理 
print ("----------8. pmvs2----------") 
pRecons = subprocess.Popen( [os.path.join(PMVS_BIN, "pmvs2"),  reconstruction_dir+"/PMVS/", "pmvs_options.txt"] )  # 注：不要修改pmvs_options.txt文件名 
pRecons.wait() 
#*注：执行后会在./PMVS/models文件夹中生成一个pmvs_options.txt.ply点云文件，用meshlab打开即可看到重建出来的彩色稠密点云。
#执行三维重建测试 
python 3dr_test.py

# 1.  把openMVG生成的SfM_Data转为适用于PMVS输入格式的文件 
cd openMVG/ImageDataset_SceauxCastle/reconstruction_global/ 
openMVG_main_openMVG2PMVS -i sfm_data.bin -o ./ 
*注：执行后会在-o路径下生成一各PMVS目录，包含 models, txt, visualize 三个子目录：models为空；txt包含对应图像的txt文档，每个里面都是一个3x4的矩阵，大概是相机位姿；visualize包含11张图像，不确定是原图像还是校正过的图像  

# 2. 使用PMVS重建稠密点云、表面、纹理 
pmvs2 ./PMVS/ pmvs_options.txt    # 注：不要修改pmvs_options.txt文件名 

*注：执行后会在./PMVS/models文件夹中生成一个pmvs_options.txt.ply点云文件，用meshlab打开即可看到重建出来的彩色稠密点云。