MVSNet depthfusion配置流程

原文内容

R/MVSNet itself only produces per-view depth maps. To generate the 3D point cloud, we need to apply depth map filter/fusion for post-processing. As our implementation of this part is depended on the Altizure internal library, currently we could not provide the corresponding code.
Fortunately, depth map filter/fusion is a general step in MVS reconstruction, and there are similar implementations in other open-source MVS algorithms. We provide the script depthfusion.py to utilize fusibile for post-processing (thank Silvano Galliani for the excellent code!).

To run the post-processing:

Check out the modified version fusibile git clone https://github.com/YoYo000/fusibile
Install fusibile by cmake . and make, which will generate the executable at FUSIBILE_EXE_PATH
Run post-processing (–prob_threshold 0.8 if using 3DCNNs): python depthfusion.py --dense_folder TEST_DATA_FOLDER --fusibile_exe_path FUSIBILE_EXE_PATH --prob_threshold 0.3
The final point cloud is stored in TEST_DATA_FOLDER/points_mvsnet/consistencyCheck-TIME/final3d_model.ply.

使用Origin Fusible代码融合出来的点云颜色是没有的，这里YaoYao通过修改了代码使得Gipuma融合出来的点云是由颜色的, 下面是YaoYao给出的代码: click here

CMakeList配置需求

cmake_minimum_required (VERSION 3.9)
project (fusibile)# Enable C++11 globally
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)# Support IDEs: https://cliutils.gitlab.io/modern-cmake/chapters/features/ides.html
set_property(GLOBAL PROPERTY USE_FOLDERS ON)
set_property(GLOBAL PROPERTY PREDEFINED_TARGETS_FOLDER "cmake-default-targets")find_package(OpenCV REQUIRED )
find_package(CUDA 6.0 REQUIRED ) # For Cuda Managed Memory and c++11if(NOT DEFINED CMAKE_CUDA_STANDARD)set(CMAKE_CUDA_STANDARD 11)set(CMAKE_CUDA_STANDARD_REQUIRED ON)
endif()include_directories(${OpenCV_INCLUDE_DIRS})
include_directories(.)# from https://en.wikipedia.org/wiki/CUDA#GPUs_supported
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-O3 --use_fast_math --ptxas-options=-v --compiler-options -Wall -gencode arch=compute_30,code=sm_30 -gencode arch=compute_32,code=sm_32 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_62,code=sm_62 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_72,code=sm_72 -gencode arch=compute_75,code=sm_75)
#set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-O3 --use_fast_math --ptxas-options=-v -std=c++11 --compiler-options -Wall -gencode arch=compute_52,code=sm_52)if(CMAKE_COMPILER_IS_GNUCXX)add_definitions(-std=c++11)add_definitions(-Wall)add_definitions(-Wextra)add_definitions(-pedantic)add_definitions(-Wno-unused-function)set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -O3 -ffast-math -march=native") # extend release-profile with fast-math#add_definitions(-march=native)
endif()# for YouCompleteMe
set( CMAKE_EXPORT_COMPILE_COMMANDS 1 )# For compilation ...
# Specify target & source files to compile it from
cuda_add_executable(fusibilecameraGeometryUtils.hvector_operations.hcamera.hglobalstate.halgorithmparameters.hcameraparameters.hlinestate.hdisplayUtils.hmathUtils.hfileIoUtils.hfusibile.cumain.cpp)
# https://cliutils.gitlab.io/modern-cmake/chapters/packages/OpenMP.html
find_package(OpenMP)# For linking ...
# Specify target & libraries to link it with
target_link_libraries(fusibile${OpenCV_LIBS}OpenMP::OpenMP_CXX)include(FeatureSummary)
feature_summary(WHAT ALL)

针对CMakeList.txt文件修改

官方文件由于是14年的文件，那时候还不支持2080ti等算力的显卡。我通过cmake . 和make 编译得到的fusibile程序，运行报no kernel image is available for execution on the device的错误，因此为了兼容我的显卡以及多种其他算力不同cuda版本的显卡，我修改了CMAKELIST.txt文件。

将下面内容注释掉:

set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-O3 --use_fast_math --ptxas-options=-v -std=c++11 --compiler-options -Wall -gencode arch=compute_52,code=sm_52)

替换为了如下内容:

set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-O3 --use_fast_math --ptxas-options=-v --compiler-options -Wall -gencode arch=compute_30,code=sm_30 -gencode arch=compute_32,code=sm_32 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_62,code=sm_62 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_72,code=sm_72 -gencode arch=compute_75,code=sm_75)

目前好像还不支持3090等30系显卡，但是20系显卡都支持了，后续的需要进行修改就好了.

具体配置

将https://github.com/YoYo000/fusibile的代码clone下来
前提是配置好下面所需要的配置：CMAKE、OPenCV和CUDA
通过cmake.和make来安装fusible
- 进入fusible文件夹
- 输入cmake ., 得到如下数据

-- The following OPTIONAL packages have been found:* OpenMP-- The following REQUIRED packages have been found:* OpenCV* Threads* CUDA (required version >= 6.0)-- Configuring done
-- Generating done
-- Build files have been written to: /file/sync_files/MVSNet/fusible

输入make, 编译完成之后文件夹会出现一个fusible文件没有后缀，表示成功了。
如果需要的话，需要使用如下命令来清除缓存

rm -rf CMakeCache.txt

Linux安装CMAKE

通过如下命令查看Linux位数

getconf LONG_BIT

获取cmake源码包，先创了一个文件夹来存储cmake, 根据官方配置需求文件，得到我们需要一个CMAKE版本大于3.9

mkdir appcd app

然后要去CMAKE官网去获取对应的CMAKE链接, 使用如下命令下载:

wget install https://github.com/Kitware/CMake/releases/download/v3.21.2/cmake-3.21.2.tar.gz

这里我下载的是3.21版本的CMAKE.

解压源码

tar xzvf cmake-3.21.2.tar.gz

安装gcc(Ubuntu使用apt-get安装，Linux使用yum安装)

# Ubuntu
apt-get install gcc# Linux
yum install gcc-c++

安装cmake，先进入解压后的cmake的目录

cd cmake-3.21.2./bootstrap

如果编译出现如下信息:

 Could NOT find OpenSSL

Ubuntu系统可以执行如下命令安装相关依赖:

apt-get update
apt-get install libssl-dev

安装完成之后再重新编译就好了。

make install

完成之后使用如下命令查看cmake版本

cmake --version

Linux安装OpenCV

按照之前的步骤安装完cmake
安装依赖环境

apt-get install build-essential libgtk2.0-dev libavcodec-dev libavformat-dev libjpeg-dev libswscale-dev libtiff5-devapt-get install libgtk2.0-devapt-get install pkg-config

去OpenCV官网下载源码，这里我是将源码下载下来，然后在上传到服务器上的，可以使用wget来进行源码的下载，命令如下:

wget install https://github.com/opencv/opencv/archive/4.5.3.zip

解压完文件

unzip -n opencv-4.5.3.zip

解压完文件之后，进入目录，创建build目录

cd opencv-4.5.3mkdir build

使用如下命令清除CMakeCache缓存

rm -rf CMakeCache.txt

这个方法是进入到build文件夹之后，使用cmake出现如下错误时使用:

CMake Error: The source directory "/file/app/opencv-4.5.3/build" does not appear to contain CMakeLists.txt.

进入build文件夹，使用cmake命令

cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..

如果安装了Anaconda建议使用如下命令:

cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_SHARED_LIBS=OFF -D WITH_OPENMP=ON -D ENABLE_PRECOMPILED_HEADERS=OFF ..

切记不能在cmake配置下面的环境变量，否则会报错！！！
类似下面这种

  Some of these libraries may not be found correctly. Call Stack (most recent call first):   cmake/OpenCVModule.cmake:1273 (ocv_add_executable)   modules/highgui/CMakeLists.txt:272 (ocv_add_accuracy_tests)

执行结束之后，在使用make进行编译

make -j8

使用如下命令进行安装

make install

配置环境，使用vim打开/etc/ld.so.conf，在最后一行加上/usr/local/lib

vim /etc/ld.so.confinclude /usr/local/lib

然后运行如下命令生效:

ldconfig

修改bash.bashrc文件

vim /etc/bash.bashrc

在文件末尾添加上如下内容:

PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig
export PKG_CONFIG_PATH

然后在命令输入如下命令生效:

source /etc/bash.bashrc

检查是否生效

cd /usr/local/lib
ls

问题

报错GL/gl.h: No such file or directory

使用如下命令安装:

sudo apt-get install mesa-common-dev
sudo apt-get install libgl1-mesa-dev libglu1-mesa-dev