常见开源多视图立体算法运行脚本记录

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1. MVE

项目主页 https://www.gcc.tu-darmstadt.de/home/proj/mve/

Github地址 https://github.com/simonfuhrmann/mve

#!/bin/bash

workspace_path=/root/test_result/mve_result
image_dir=${workspace_path}/${1}
scene_dir=${workspace_path}/${2}
mve=/root/misc_codes/mve/apps

maxpixel=20000000

intrinsic_fp="2759.48,0,0,0.4950,0.4916,0.9983" # fountain-p11
intrinsic_tp="1520.40,0,0,0.4724,0.5143,0.9964" # temple
intrinsic_eth_pipe="3430.27,0,0,0.5015,0.4969,1.0003" # eth3d pipes
intrinsic_dtu="2892.33,0,0,0.5145,0.5159,1.0032" # dtu dataset
intrinsic_tanks=""
intrinsic=${intrinsic_tanks}
# --init-intrinsics=${intrinsic} ${mve}/makescene/makescene --original         --images-only ${image_dir}         --max-pixels=${maxpixel}         ${scene_dir} &&

# --fixed-intrinsics ${mve}/sfmrecon/sfmrecon --max-pixels=${maxpixel}         --verbose-ba ${scene_dir} &&

${mve}/dmrecon/dmrecon --neighbors=9--scale=0--max-pixels=${maxpixel}         --local-neighbors=6--keep-dz         --progress=fancy ${scene_dir} &&

${mve}/scene2pset/scene2pset -F0 ${scene_dir} ${scene_dir}/pset-L0.ply &&

${mve}/fssrecon/fssrecon ${scene_dir}/pset-L0.ply ${scene_dir}/surface-L0.ply &&

${mve}/meshclean/meshclean --threshold=8.0 --delete-scale ${scene_dir}/surface-L0.ply ${scene_dir}/surface-clean.ply

2. SMVS

项目主页 https://www.gcc.tu-darmstadt.de/home/proj/smvs/smvs.en.jsp

Github地址 https://github.com/flanggut/smvs

#!/bin/bash

workspace_path=/root/test_result/smvs_result
image_dir=${workspace_path}/${1}
scene_dir=${workspace_path}/${2}
mve=/root/misc_codes/mve/apps
smvs=/root/misc_codes/smvs/smvsrecon

maxpixel=2000000

intrinsic_fp="2759.48,0,0,0.4950,0.4916,0.9983" # fountain-p11
intrinsic_tp="1520.40,0,0,0.4724,0.5143,0.9964" # temple
intrinsic_eth_pipe="3430.27,0,0,0.5015,0.4969,1.0003" # eth3d pipes
intrinsic_dtu="2892.33,0,0,0.5145,0.5159,1.0032" # dtu dataset
intrinsic_tanks="2304.00,0,0,0.5,0.5,1.0000" # Manually set intrinsic
intrinsic=${intrinsic_fp}

${mve}/makescene/makescene --original         --images-only ${image_dir}         --max-pixels=${maxpixel}         --init-intrinsics=${intrinsic}         ${scene_dir} &&

${mve}/sfmrecon/sfmrecon --max-pixels=${maxpixel}         --fixed-intrinsics         --verbose-ba ${scene_dir} &&

${smvs} ${scene_dir} &&

${mve}/fssrecon/fssrecon ${scene_dir}/pset-L0.ply ${scene_dir}/surface-L0.ply &&

${mve}/meshclean/meshclean --threshold=8.0 --delete-scale        ${scene_dir}/surface-L0.ply        ${scene_dir}/surface-clean.ply

3. openMVG+openMVS

多视图立体几何基础库 openMVG https://github.com/openMVG/openMVG

稠密重建库 openMVS https://github.com/cdcseacave/openMVS

#!/bin/bash

workspace_path=/root/test_result/openmvs_result/${1}
image_dir=${workspace_path}/images
recon_dir=${workspace_path}/reconstruct
match_dir=${recon_dir}/matches
openmvg=/root/misc_codes/openMVG/openmvg-bin/bin
openmvs=/root/misc_codes/openMVS/openmvs-build/bin

maxres=6400
minres=480

intrinsic_fp="2759.48;0;1520.69;0;2764.16;1006.81;0;0;1" # fountain-p11
intrinsic_tp="1520.40;0;302.32;0;1525.90;246.87;0;0;1" # temple
intrinsic_eth_pipe="3430.27;0;3119.2;0;3429.23;2057.75;0;0;1" # eth3d pipes
intrinsic_dtu="2892.33;0;823.21;0;2883.17;619.07;0;0;1" # for all dtu datasets
intrinsic_tanks="2304.00;0;960;0;2304.00;540;0;0;1" # Manually set intrinsic
intrinsic=${intrinsic_dtu}

mkdir ${recon_dir} && mkdir ${match_dir}
${openmvg}/openMVG_main_SfMInit_ImageListing -i ${image_dir} -o ${match_dir}         --camera_model 1--intrinsics ${intrinsic}         --group_camera_model 1

${openmvg}/openMVG_main_ComputeFeatures -i ${match_dir}/sfm_data.json         --outdir ${match_dir}         --describerPreset HIGH

${openmvg}/openMVG_main_ComputeMatches -i ${match_dir}/sfm_data.json         --out_dir ${match_dir}         --nearest_matching_method ANNL2

${openmvg}/openMVG_main_IncrementalSfM -i ${match_dir}/sfm_data.json         --matchdir ${match_dir}         --outdir ${recon_dir}         --camera_model 1--refineIntrinsics NONE
        # --refineIntrinsics "ADJUST_FOCAL_LENGTH|ADJUST_PRINCIPAL_POINT"

${openmvg}/openMVG_main_ComputeSfM_DataColor -i ${recon_dir}/sfm_data.bin         -o ${recon_dir}/colorized.ply

${openmvg}/openMVG_main_ComputeStructureFromKnownPoses -i ${recon_dir}/sfm_data.bin         --match_dir ${match_dir}         --match_file ${match_dir}/matches.f.bin         --output_file ${recon_dir}/robust.bin

${openmvg}/openMVG_main_ComputeSfM_DataColor -i ${recon_dir}/robust.bin         -o ${recon_dir}/robust_colorized.ply

# outfile is the file name to save converted result
# outdir is the path to save undistorted images
${openmvg}/openMVG_main_openMVG2openMVS --sfmdata ${recon_dir}/sfm_data.bin         --outfile ${recon_dir}/scene.mvs         --outdir ${recon_dir}

${openmvs}/DensifyPointCloud --working-folder ${recon_dir}         -i ${recon_dir}/scene.mvs         --max-resolution=${maxres}         --min-resolution=${minres}         --number-views=6
# free-space-support is for textureless region
${openmvs}/ReconstructMesh --working-folder ${recon_dir}         -i ${recon_dir}/scene_dense.mvs         --free-space-support 1

${openmvs}/RefineMesh --working-folder ${recon_dir}         -i ${recon_dir}/scene_dense_mesh.mvs         --min-resolution ${minres}         --max-views 9--scales 5--planar-vertex-ratio 5

${openmvs}/TextureMesh --working-folder ${recon_dir}         -i ${recon_dir}/scene_dense_mesh.mvs         --min-resolution ${minres}         --cost-smoothness-ratio 0.3

4. COLMAP

项目主页 https://demuc.de/colmap/

Github地址 https://github.com/colmap/colmap

#!/bin/bash

colmap=/root/misc_codes/colmap/colmap-bin/bin/colmap
workspace=/root/test_result/colmap_result/${1}
images=${workspace}/images
database_path=${workspace}/database.db
sparse_path=${workspace}/sparse
dense_path=${workspace}/dense
maxsize=2000
maxfeature=8192

intrinsic_fp="2759.48,2764.16,1520.69,1006.81" # fountain-p11
intrinsic_tp="1520.40,1525.90,302.32,246.87" # temple
intrinsic_dtu="2892.33,2883.17,823.21,619.07" # all dtu dataset
intrinsic_tanks=""
intrinsic=${intrinsic_dtu}

# --ImageReader.camera_params ${intrinsic} ${colmap} feature_extractor         --database_path ${database_path}         --image_path ${images}         --ImageReader.camera_model PINHOLE         --ImageReader.camera_params ${intrinsic}         --ImageReader.single_camera 1--SiftExtraction.max_image_size ${maxsize}         --SiftExtraction.max_num_features ${maxfeature}

${colmap} exhaustive_matcher --database_path ${database_path}         --SiftMatching.guided_matching 0mkdir ${sparse_path}
${colmap} mapper --database_path ${database_path}         --image_path ${images}         --output_path ${sparse_path}         --Mapper.ba_refine_principal_point falsemkdir ${dense_path} &&
${colmap} image_undistorter --image_path ${images}         --input_path ${sparse_path}/0--output_path ${dense_path}         --output_type COLMAP         --max_image_size ${maxsize} &&

${colmap} patch_match_stereo --workspace_path ${dense_path}         --workspace_format COLMAP         --PatchMatchStereo.max_image_size ${maxsize}         --PatchMatchStereo.window_radius 9--PatchMatchStereo.geom_consistency 1--PatchMatchStereo.filter_min_ncc 0.07 &&

${colmap} stereo_fusion --workspace_path ${dense_path}         --input_type geometric         --output_path ${dense_path}/fused.ply &&

${colmap} poisson_mesher --input_path ${dense_path}/fused.ply         --output_path ${dense_path}/meshed-poisson.ply

${colmap} delaunay_mesher --input_path ${dense_path}         --input_type dense         --output_path ${dense_path}/meshed-delaunay.ply

colmap recon script(from tanks and temples)

#!/bin/bash

# ----------------------------------------------------------------------------
# -                   TanksAndTemples Website Toolbox                        -
# -                    http://www.tanksandtemples.org                        -
# ----------------------------------------------------------------------------
# The MIT License (MIT)
#
# Copyright (c) 2017
# Arno Knapitsch <arno.knapitsch@gmail.com >
# Jaesik Park <syncle@gmail.com>
# Qian-Yi Zhou <Qianyi.Zhou@gmail.com>
# Vladlen Koltun <vkoltun@gmail.com>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
# ----------------------------------------------------------------------------
#
# This script generates a COLMAP reconstruction from a numbe rof input imagess
# Usage: sh get_colmap_reconstruction.sh <COLMAP-exe-directory> <image-set-directory> <project-directory>

colmap_folder=$1/
iname=$2/
outf=$3/

DATABASE=${outf}sample_reconstruction.db

PROJECT_PATH=${outf}
mkdir -p ${PROJECT_PATH}
mkdir -p ${PROJECT_PATH}/images

cp -n ${iname}*.jpg ${PROJECT_PATH}/images

${colmap_folder}/colmap feature_extractor     --database_path ${DATABASE}     --image_path ${PROJECT_PATH}/images     --ImageReader.camera_model RADIAL     --ImageReader.single_camera 1--SiftExtraction.use_gpu 1
    
${colmap_folder}/colmap exhaustive_matcher     --database_path ${DATABASE}     --SiftMatching.use_gpu 1mkdir ${PROJECT_PATH}/sparse
${colmap_folder}/colmap mapper     --database_path ${DATABASE}     --image_path ${PROJECT_PATH}/images     --output_path ${PROJECT_PATH}/sparse

mkdir ${PROJECT_PATH}/dense

${colmap_folder}/colmap image_undistorter     --image_path ${PROJECT_PATH}/images     --input_path ${PROJECT_PATH}/sparse/0/--output_path ${PROJECT_PATH}/dense     --output_type COLMAP --max_image_size 1500

${colmap_folder}/colmap patch_match_stereo     --workspace_path $PROJECT_PATH/dense     --workspace_format COLMAP     --PatchMatchStereo.geom_consistency true

${colmap_folder}/colmap stereo_fusion     --workspace_path $PROJECT_PATH/dense     --workspace_format COLMAP     --input_type geometric     --output_path $PROJECT_PATH/dense/fused.ply

上述几个脚本都放到了我的github上，地址是：https://github.com/philleer/program_test/tree/mvs_script

Ubuntu 18.04 LTS 亲自测试有效，欢迎补充

原文：https://www.cnblogs.com/phillee/p/13094478.html

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