【3D视觉创新应用竞赛作品系列】牲畜体表信息的三维重建，代码详尽
作者|| 陆杰
编辑||3D视觉开发者社区
✨如果觉得文章内容不错，别忘了三连支持下哦~
文章目录
- 导语
- 一、环境配置
-
- OpenCV在Arm平台上编译
- OpenCV+OpenNI2配置
- 二、数据获取
-
- 获取深度图
- 获取彩色图
- 获取彩色点云
- 三、数据配准
-
- 配准代码
- 四、姿势归一化
-
- 软件处理方法（自主研发）
- 五、三维重建
-
- 数据准备
- 初始化
- 非刚配准
- 结果评价
- 六、Demo演示
导语
本文为“3D视觉创新应用（三维重建）竞赛”作品集系列之一，该作品由来自中国农业大学团队完成，团队成员：陆杰、苏杨、都敖。全文约2054个字，阅读时长约5分钟，旨在为更多开发者提供学习参考。
一、环境配置
奥比中光OpenNISDK安装（Linux）
奥比中光针对ZaroP1开发板和深度摄像机提供了相关的OpenNI2的SDK,针对Windows/Linux/Android不同平台提供了相关的安装文档.根据官方文档将OpenNI2配置完成，注意官方文档中说Linux平台内置驱动不需要额外安装，但是需要仔细阅读README文件：
OpenNI
------
Website: http://structure.io/openni
Building Prerequisites(前置依赖)
======================
Linux
-----
- GCC 4.x
From: http://gcc.gnu.org/releases.html
Or via apt:
sudo apt-get install g++
- Python 2.6+/3.x
From: http://www.python.org/download/
Or via apt:
sudo apt-get install python
- LibUSB 1.0.x
From: http://sourceforge.net/projects/libusb/files/libusb-1.0/
Or via apt:
sudo apt-get install libusb-1.0-0-dev
- LibUDEV
sudo apt-get install libudev-dev
- JDK 6.0
From: http://www.oracle.com/technetwork/java/javase/downloads/jdk-6u32-downloads-1594644.html
Or via apt:
sudo apt-get install openjdk-6-jdk
- FreeGLUT3
From: http://freeglut.sourceforge.net/index.php#download
Or via apt:
sudo apt-get install freeglut3-dev
- Doxygen
From: http://www.stack.nl/~dimitri/doxygen/download.html#latestsrc
Or via apt:
sudo apt-get install doxygen
- GraphViz
From: http://www.graphviz.org/Download_linux_ubuntu.php
Or via apt:
sudo apt-get install graphviz
将以上的依赖安装完成之后，运行其中示例代码还需要安装对应的USB驱动，驱动安装方法如下此方法将同目录下的USB驱动文件拷贝到系统目录中，并自动生成环境文件指明OpenNI的路径此方法将同目录下的USB驱动文件拷贝到系统目录中，并自动生成环境文件指明OpenNI的路径,进入安装包OpenNI目录中:
orbbec@localhost:OpenNI$ ./install.sh
最后，在安装文件中找到NiViewer运行文件，若文件不能运行，查看chmod权限。
sudo ./NiViewer
结果图片
经过以上的配置过程，OpenNI的环境就配置完成了。
OpenCV在Arm平台上编译
在运用开发板设备获取数据的时候，通常运用OpenNI2获取数据流，通过OpenCV对数据流进行转换，生成RGB图片和深度图片。
源文件官方地址，本文用的是OpenCV3.4.3版本。OpenCV编译的方法可通过百度搜索，编译完成后对orbbec中OpenNI2SDK和opencv进行配置。
OpenCV+OpenNI2配置
根据官方给出的示例代码，进行编写。由于官方的代码是在Makefile文件中进行编写，所以opencv也需要在其中编写。根据Makefile文件编写规则进行编写。
Includes
CFLAGS = ${shell pkg-config opencv --cflags}
#lib
LDFLAGS = ${shell pkg-config opencv --libs}
其中pkg-config需要在系统中配置opencv.pc文件，文件内容，在安装的文件中可以找到，若没有则可能默认没有生成（opencv4之后的版本默认不生成），需要在编译的时候设置。
opencv环境配置没有问题后，在orbbec提供的OpenNI2的文件中示例代码中进行整合开发。文件位置在：
/OpenNI/Samples/
选择其中SimpleViewer文件夹中的示例代码进行编写。在CommonCppMakefile文件夹中找到CFLAGS和LDFLAGS变量位置，在其后加上：
#Includes
CFLAGS += ${shell pkg-config opencv --cflags}
#lib
LDFLAGS += ${shell pkg-config opencv --libs}
此外还需要在Makefile文件中加入OpenNI头文件的路径和Redist的路径，这就是在配置OpenNI时运行install.sh文件生成的环境文件中的内容，也在同级目录下。其后编译即可通过。接下来就可以使用opencv对数据进行获取保存了。
二、数据获取
根据上文环境配置的结果，对数据获取部分的代码进行调整，由于官方OpenNI2代码运用Makefile进行管理，所以将OpenCV的配置也运用Makefile进行管理。本文以官方示例代码SimpleViewer文件加下的文件作为基础，对其中的Makefile进行配置。
首先，“Makefile”文件内容进行修改，第3、4行为添加部分，内容是官方OpenNI2的相关文件路径:
include CommonDefs.mak
BIN_DIR = Bin
OPENNI2_INCLUDE=/home/orbbec/Desktop/OpenNI-Linux-Arm64-2.3.0.65/OpenNI-Linux-Arm64-2.3.0.65/include/
OPENNI2_REDIST=/home/orbbec/Desktop/OpenNI-Linux-Arm64-2.3.0.65/OpenNI-Linux-Arm64-2.3.0.65/Redist/
INC_DIRS = \
../../Include \
../../ThirdParty/GL/ \
../Common
SRC_FILES = *.cpp
ifeq ("$(OSTYPE)","Darwin")
CFLAGS += -DMACOS
LDFLAGS += -framework OpenGL -framework GLUT
else
CFLAGS += -DUNIX -DGLX_GLXEXT_LEGACY
USED_LIBS += glut GL
endif
USED_LIBS += OpenNI2
EXE_NAME = SimpleViewer
CFLAGS += -Wall
ifndef OPENNI2_INCLUDE
$(error OPENNI2_INCLUDE is not defined. Please define it or 'source' the OpenNIDevEnvironment file from the installation)
else ifndef OPENNI2_REDIST
$(error OPENNI2_REDIST is not defined. Please define it or 'source' the OpenNIDevEnvironment file from the installation)
endif
INC_DIRS += $(OPENNI2_INCLUDE)
include CommonCppMakefile
.PHONY: copy-redist
copy-redist:
cp -R $(OPENNI2_REDIST)/* $(OUT_DIR)
$(OUTPUT_FILE): copy-redist
接着对“CommonCppMakefile”文件进行修改，45、46行为修改内容，添加的内容主要为OpenCV的编译文件的路径，由环境配置可知，在pkgconfig中配置OpenCV.pc文件后在系统中调用相关命令即可给出OpenCV安装文件的路径：
# take this file's dir
COMMON_CPP_MAKE_FILE_DIR = $(dir $(lastword $(MAKEFILE_LIST)))
include $(COMMON_CPP_MAKE_FILE_DIR)CommonDefs.mak
# define a function to figure .o file for each source file (placed under intermediate directory)
SRC_TO_OBJ = $(addprefix ./$(INT_DIR)/,$(addsuffix .o,$(notdir $(basename $1))))
# create a list of all object files
OBJ_FILES = $(call SRC_TO_OBJ,$(SRC_FILES_LIST))
# define a function to translate any source file to its dependency file (note that the way we create
# dep files, as a side affect of compilation, always puts the files in the INT_DIR with suffix .d)
SRC_TO_DEP = $(addprefix ./$(INT_DIR)/,$(addsuffix .d,$(notdir $(basename $1))))
# create a list of all dependency files
DEP_FILES = $(call SRC_TO_DEP,$(SRC_FILES_LIST))
# older version of gcc doesn't support the '=' symbol in include dirs, so we replace it ourselves with sysroot
INC_DIRS_FROM_SYSROOT = $(patsubst =/%,$(TARGET_SYS_ROOT)/%,$(INC_DIRS))
# append the -I switch to each include directory
INC_DIRS_OPTION = $(foreach dir,$(INC_DIRS_FROM_SYSROOT),-I$(dir))
# append the -L switch to each library directory
LIB_DIRS_OPTION = $(foreach dir,$(LIB_DIRS),-L$(dir)) -L$(OUT_DIR)
# append the -l switch to each library used
USED_LIBS_OPTION = $(foreach lib,$(USED_LIBS),-l$(lib))
# append the -D switch to each define
DEFINES_OPTION = $(foreach def,$(DEFINES),-D$(def))
# tell compiler to use the target system root
ifdef TARGET_SYS_ROOT
CFLAGS += --sysroot=$(TARGET_SYS_ROOT)
LDFLAGS += --sysroot=$(TARGET_SYS_ROOT)
endif
# set Debug / Release flags
ifeq "$(CFG)" "Debug"
CFLAGS += -O0 -g
endif
ifeq "$(CFG)" "Release"
CFLAGS += -O2 -DNDEBUG
endif
CFLAGS += $(INC_DIRS_OPTION) $(DEFINES_OPTION)
CFLAGS += -fPIC -fvisibility=hidden
ifneq "$(ALLOW_WARNINGS)" "1"
CFLAGS += -Werror
ifeq ("$(OSTYPE)","Darwin")
CFLAGS += -Wno-deprecated-declarations -Wno-unused-private-field -Wno-unused-const-variable
endif
endif
LDFLAGS += $(LIB_DIRS_OPTION) $(USED_LIBS_OPTION)
##############this is out method######################
CFLAGS += ${shell pkg-config opencv --cflags}
LDFLAGS += ${shell pkg-config opencv --libs}
#####################################
# some lib / exe specifics
ifneq "$(LIB_NAME)" ""
OUTPUT_NAME = lib$(LIB_NAME).so
ifneq ("$(OSTYPE)","Darwin")
LDFLAGS += -Wl,--no-undefined
OUTPUT_NAME = lib$(LIB_NAME).so
OUTPUT_COMMAND = $(CXX) -o $(OUTPUT_FILE) $(OBJ_FILES) $(LDFLAGS) -shared
else
LDFLAGS += -undefined error
OUTPUT_NAME = lib$(LIB_NAME).dylib
OUTPUT_COMMAND = $(CXX) -o $(OUTPUT_FILE) $(OBJ_FILES) $(LDFLAGS) -dynamiclib -headerpad_max_install_names -install_name $(OUTPUT_NAME)
endif
endif
ifneq "$(EXE_NAME)" ""
OUTPUT_NAME = $(EXE_NAME)
# We want the executables to look for the .so's locally first:
LDFLAGS += -Wl,-rpath ./
OUTPUT_COMMAND = $(CXX) -o $(OUTPUT_FILE) $(OBJ_FILES) $(LDFLAGS)
endif
ifneq "$(SLIB_NAME)" ""
OUTPUT_NAME = lib$(SLIB_NAME).a
ifneq ("$(OSTYPE)","Darwin")
OUTPUT_COMMAND = $(AR) -cq $(OUTPUT_FILE) $(OBJ_FILES)
else
OUTPUT_COMMAND = libtool -static -o $(OUTPUT_FILE) $(OBJ_FILES)
endif
endif
define CREATE_SRC_TARGETS
# create a target for the object file (the CXX command creates both an .o file
# and a .d file)
ifneq ("$(OSTYPE)","Darwin")
$(call SRC_TO_OBJ,$1) : $1 | $(INT_DIR)
$(CXX) -MD -MP -MT "$(call SRC_TO_DEP,$1) $$@" -c $(CFLAGS) -o $$@ $$<
else
$(call SRC_TO_OBJ,$1) : $1 | $(INT_DIR)
$(CXX) -x c++ -c $(CFLAGS) -o $$@ $$<
endif
endef
#############################################################################
# Targets
#############################################################################
.PHONY: clean-objs clean-defs
include $(COMMON_CPP_MAKE_FILE_DIR)CommonTargets.mak
# create targets for each source file
$(foreach src,$(SRC_FILES_LIST),$(eval $(call CREATE_SRC_TARGETS,$(src))))
# include all dependency files (we don't need them the first time, so we can use -include)
-include $(DEP_FILES)
$(OUTPUT_FILE): $(OBJ_FILES)
$(OUTPUT_COMMAND)
clean-objs:
rm -rf $(OBJ_FILES)
clean-defs:
rm -rf $(DEP_FILES)
clean: clean-objs clean-defs
这样本文以官方SimpleViewer文件夹为基础的Makefile相关文件的编写就完成了，接着就是对文件中主函数文件main.cpp进行代码编写，这样就可以结合openNI和OpenCV进行数据获取。
获取深度图
获取深度图主要用到了openni的接口获取数据流，然后将获取的数据流通过opencv接口转换成深度图像。
#include
#include "Viewer.h"
#include
#include
#include
#include
#include
#include
#include
using namespace std;
using namespace openni;
#define SAMPLE_READ_WAIT_TIMEOUT 2000 //2000ms
int wasKeyboardHit();
int main(int argc, char** argv)
{
openni::Status rc = openni::STATUS_OK;
openni::Status rc_d = openni::STATUS_OK;
openni::Device device;
openni::VideoStream depth, color;
const char* deviceURI = openni::ANY_DEVICE;
rc = openni::OpenNI::initialize();
printf("After initialization:\n%s\n", openni::OpenNI::getExtendedError());
rc = device.open(deviceURI);
if (rc != openni::STATUS_OK)
{
printf("SimpleViewer: Device open failed:\n%s\n", openni::OpenNI::getExtendedError());
openni::OpenNI::shutdown();
return 1;
}
rc = depth.create(device, openni::SENSOR_DEPTH);
if (rc == openni::STATUS_OK)
rc = depth.start();
openni::VideoFrameRef frameDepth;
openni::DepthPixel* pDepth;
cout<<"stream start, press any key to stop..."< 获取彩色图
获取彩色图不能通过openni接口 readframe()，需要使用opencv的 VideoCapture相关接口。
#include
using namespace cv;
int main()
{
VideoCapture videoCapture;
videoCapture.open(cv::CAP_ANY);
if (!videoCapture.isOpened())
{
printf("open UVC color camera failed.");
}
//set codec format
videoCapture.set(CV_CAP_PROP_FOURCC, CV_FOURCC('M', 'J', 'P', 'G'));
//set resolution
videoCapture.set(CV_CAP_PROP_FRAME_WIDTH, 640);
videoCapture.set(CV_CAP_PROP_FRAME_HEIGHT, 480);
for(int i=0;i<10;i++)
{
Mat frame;
videoCapture >> frame;
Vec3b &bgr = frame.at(frame.rows/2, frame.cols/2);
//print the r g b value of the middle pixel of the frame
printf("r = %02d, g = %02d, b = %02d\n",
bgr[2] & 0xff,
bgr[1] & 0xff,
bgr[0] & 0xff);
waitKey(30); //delay 30ms
imwrite("/home/orbbec/Desktop/saveRGBImg/Bin/Arm64-Release/saved_img.png",frame);
}
return 0;
}
获取彩色点云
根据opencv中videoCapture获得的带有彩色Mat数据做为参数输入，最终在文件写入时与坐标一起写入至ply文件中：
#include
#include
#include "OpenNI.h"
#include "OniSampleUtilities.h"
#include
using namespace openni;
using namespace cv;
#define MIN_DISTANCE 20 //µ¥Î»ºÁÃ×
#define MAX_DISTANCE 4000 //µ¥Î»ºÁÃ×
#define RESOULTION_X 640.0 //±ê¶šÊ±µÄ·Ö±æÂÊ
#define RESOULTION_Y 480.0 //±ê¶šÊ±µÄ·Ö±æÂÊ
#define MAX_FRAME_COUNT 50
typedef struct xnIntrinsic_Params
{
xnIntrinsic_Params() :
c_x(320.0), c_y(240.0), f_x(480.0), f_y(480.0)
{}
xnIntrinsic_Params(float c_x_, float c_y_, float f_x_, float f_y_) :
c_x(c_x_), c_y(c_y_), f_x(f_x_),f_y(f_y_)
{}
float c_x; //uÖáÉϵĹéÒ»»¯œ¹Ÿà
float c_y; //vÖáÉϵĹéÒ»»¯œ¹Ÿà
float f_x; //Ö÷µãx×ø±ê
float f_y; //Ö÷µãy×ø±ê
}xIntrinsic_Params;
xIntrinsic_Params g_IntrinsicParam; //ŽæŽ¢Ïà»úÄڲεÄÈ«ŸÖ±äÁ¿
void getCameraParams(openni::Device& Device, xIntrinsic_Params& IrParam)
{
OBCameraParams cameraParam;
int dataSize = sizeof(cameraParam);
memset(&cameraParam, 0, sizeof(cameraParam));
openni::Status rc = Device.getProperty(openni::OBEXTENSION_ID_CAM_PARAMS, (uint8_t *)&cameraParam, &dataSize);
if (rc != openni::STATUS_OK)
{
std::cout << "Error:" << openni::OpenNI::getExtendedError() << std::endl;
return;
}
std::cout << "IrParam.f_x = " << IrParam.f_x << std::endl;
std::cout << "IrParam.f_y = " << IrParam.f_y << std::endl;
std::cout << "IrParam.c_x = " << IrParam.c_x << std::endl;
std::cout << "IrParam.c_y = " << IrParam.c_y << std::endl;
}
void convertDepthToPointCloud(const uint16_t *pDepth, Mat color, int width, int height,const char *ply_filename)
{
if (NULL == pDepth)
{
printf("depth frame is NULL!");
return;
}
FILE *fp;
fp = fopen(ply_filename, "w");
int valid_count = 0;
uint16_t max_depth = MAX_DISTANCE;
uint16_t min_depth = MIN_DISTANCE;
for (int v = 0; v < height; ++v)
{
for (int u = 0; u < width; ++u)
{
uint16_t depth = pDepth[v * width + u];
if (depth <= 0 || depth < min_depth || depth > max_depth)
continue;
valid_count += 1;
}
}
fprintf(fp, "ply\n");
fprintf(fp, "format ascii 1.0\n");
fprintf(fp, "element vertex %d\n", valid_count);
fprintf(fp, "property float x\n");
fprintf(fp, "property float y\n");
fprintf(fp, "property float z\n");
fprintf(fp, "property uchar red\n");
fprintf(fp, "property uchar green\n");
fprintf(fp, "property uchar blue\n");
fprintf(fp, "end_header\n");
float world_x, world_y, world_z;
for (int v = 0; v < height; ++v)
{
for (int u = 0; u < width; ++u)
{
uint16_t depth = pDepth[v * width + u];
Vec3b &bgr = color.at(v, u);
int color_b= bgr[0] & 0xff;
int color_g= bgr[1] & 0xff;
int color_r= bgr[2] & 0xff;
if (depth <= 0 || depth < min_depth || depth > max_depth)
continue;
float fdx = g_IntrinsicParam.f_x * ((float)(width) / RESOULTION_X);
float fdy = g_IntrinsicParam.f_y * ((float)(height) / RESOULTION_Y);
float u0 = g_IntrinsicParam.c_x * ((float)(width)/ RESOULTION_X);
float v0 = g_IntrinsicParam.c_y * ((float)(height) / RESOULTION_Y);
float tx = (u - u0) / fdx;
float ty = (v - v0) / fdy;
world_x = depth * tx;
world_y = depth * ty;
world_z = depth;
fprintf(fp, "%f %f %f %d %d %d\n", world_x, world_y, world_z, color_r,color_g,color_b);
}
}
fclose(fp);
}
int g_imageCount = 0;
void analyzeFrame(const VideoFrameRef& frame, Mat color)
{
DepthPixel* pDepth;
//¹¹ÔìµãÔÆÎÄŒþÃû
char plyFileName[256] = "";
g_imageCount++;
if (MAX_FRAME_COUNT < g_imageCount)
{
return;
}
std::stringstream filename;
filename << "pointcloud_";
filename << g_imageCount;
filename << ".ply";
filename >> plyFileName;
int middleIndex = (frame.getHeight() + 1)*frame.getWidth() / 2;
switch (frame.getVideoMode().getPixelFormat())
{
case PIXEL_FORMAT_DEPTH_1_MM:
pDepth = (DepthPixel*)frame.getData();
printf("[%08llu] %8d\n", (long long)frame.getTimestamp(),pDepth[middleIndex]);
convertDepthToPointCloud(pDepth, color, frame.getWidth(), frame.getHeight(), plyFileName);
break;
default:
printf("Unknown format\n");
}
}
int main(int argc, char* argv[])
{
//initialize openNI sdk
Status rc = OpenNI::initialize();
if (rc != STATUS_OK)
{
printf("Initialize failed\n%s\n", OpenNI::getExtendedError());
return 1;
}
//open deivce
Device device;
rc = device.open(ANY_DEVICE);
if (rc != STATUS_OK)
{
printf("Couldn't open device\n%s\n", OpenNI::getExtendedError());
return 2;
}
VideoCapture videoCapture;
videoCapture.open(cv::CAP_ANY);
if (!videoCapture.isOpened())
{
printf("open UVC color camera failed.");
}
//set codec format
videoCapture.set(CV_CAP_PROP_FOURCC, CV_FOURCC('M', 'J', 'P', 'G'));
//set resolution
videoCapture.set(CV_CAP_PROP_FRAME_WIDTH, 640);
videoCapture.set(CV_CAP_PROP_FRAME_HEIGHT, 480);
VideoStream depth;
//create depth stream
if (device.getSensorInfo(SENSOR_DEPTH) != NULL)
{
rc = depth.create(device, SENSOR_DEPTH);
if (rc != STATUS_OK)
{
printf("Couldn't create depth stream\n%s\n", OpenNI::getExtendedError());
}
}
rc = depth.start();
if (rc != STATUS_OK)
{
printf("Couldn't start the depth stream\n%s\n", OpenNI::getExtendedError());
}
openni::VideoFrameRef frameDepth;
for(int i=0;i<10;i++)
{
Mat color_frame;
videoCapture >> color_frame;
Vec3b &bgr = color_frame.at(color_frame.rows/2, color_frame.cols/2);
//print the r g b value of the middle pixel of the frame
printf("r = %02d, g = %02d, b = %02d\n",
bgr[2] & 0xff,
bgr[1] & 0xff,
bgr[0] & 0xff);
rc = depth.readFrame(&frameDepth);
analyzeFrame(frameDepth, color_frame);
if (rc != openni::STATUS_OK)
continue;
waitKey(30);
}
depth.stop();
depth.destroy();
//close device
device.close();
//shutdown OpenNI
OpenNI::shutdown();
return 0;
三、数据配准
根据上文数据获取的准备，在Zora P1开发板上进行数据获取工作，作为三维重建的基础，本文只获取了两侧的点云数据进行三维重建，在数据方面要求较低，容易获取。
配准代码
获取得到牲畜的两侧数据后，接下来需要对数据进行配准。本文配准方法主要运用两侧点云数据的同名点，对应点的获取通过点云库手动选择得出，通过对应点计算得出旋转矩阵进行配准。配准函数代码如下：
#include
#include
#include
#include
#include
#include // TicToc
#include
#include
#include
#include
#include
#include
#include
typedef pcl::PointXYZRGB PointT;
typedef pcl::PointCloud PointCloudT;
using namespace pcl;
int main(int argc,char* argv[])
{
// The point clouds we will be using
PointCloudT::Ptr cloud_in(new PointCloudT); // Original point cloud
PointCloudT::Ptr cloud_in2(new PointCloudT); // Transformed point cloud
PointCloudT::Ptr cloud_icp(new PointCloudT); // ICP output point cloud
PointCloudT::Ptr corrspond_in(new PointCloudT); // Original point cloud
PointCloudT::Ptr corrspond_out(new PointCloudT); // Transformed point cloud
PointCloudT::Ptr show_transformed_clouds(new PointCloudT);
std::mutex cloud_mutex_;
// Checking program arguments
if (argc < 3)
{
printf("Usage :\n");
printf("\t\t%s file.ply number_of_ICP_iterations\n", argv[0]);
PCL_ERROR("Provide one ply file.\n");
return (-1);
}
pcl::console::TicToc time;
time.tic();
if (pcl::io::loadPLYFile(argv[1], *cloud_in) < 0)
{
PCL_ERROR("Error loading cloud %s.\n", argv[1]);
return (-1);
}
if (pcl::io::loadPLYFile(argv[2], *cloud_in2) < 0)
{
PCL_ERROR("Error loading cloud %s.\n", argv[2]);
return (-1);
}
std::cout << "\nLoaded file " << argv[1] << " (" << cloud_in->size() << " points) in " << time.toc() << " ms\n" << std::endl;
Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity();
PointT point_in_1(-0.201973, -0.326478, 1.578000, 0, 0, 0);
PointT point_in_2(-0.328673, -0.325839, 1.616000, 0, 0, 0);
PointT point_in_3(-0.297260, -0.269961, 1.730000, 0, 0, 0);
PointT point_in_4(-0.169498, -0.276550, 1.696000, 0, 0, 0);
corrspond_in->push_back(point_in_1);
corrspond_in->push_back(point_in_2);
corrspond_in->push_back(point_in_3);
corrspond_in->push_back(point_in_4);
PointT point_in_5(0.165862, -0.074352, 1.631000, 0, 0, 0);
PointT point_in_6(0.290257, -0.068296, 1.623000, 0, 0, 0);
PointT point_in_7(0.298880, -0.133429, 1.522000, 0, 0, 0);
PointT point_in_8(0.175316, -0.138128, 1.515000, 0, 0, 0);
corrspond_out->push_back(point_in_5);
corrspond_out->push_back(point_in_6);
corrspond_out->push_back(point_in_7);
corrspond_out->push_back(point_in_8);
cout << corrspond_in->size() << endl;
cout << corrspond_out->size() << endl;
//利用SVD方法求解变换矩阵
pcl::registration::TransformationEstimationSVD TESVD;
pcl::registration::TransformationEstimationSVD::Matrix4 transformation2;
TESVD.estimateRigidTransformation(*corrspond_in, *corrspond_out, transformation2);
cout << transformation2 << endl;
pcl::transformPointCloud(*cloud_in, *show_transformed_clouds, transformation2);
*show_transformed_clouds += *cloud_in2;
pcl::PCDWriter writer;
writer.write("registered.pcd", *show_transformed_clouds, false);
return (0);
}
CMakeLists.txt文件内容如下
cmake_minimum_required(VERSION 2.6 FATAL_ERROR)
project(transform_twoside)
find_package(PCL 1.5 REQUIRED)
include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
add_executable (transform_twoside transform_twoside.cpp)
target_link_libraries (transform_twoside ${PCL_LIBRARIES})
获取的原始数据：
配准后数据：
四、姿势归一化
本方法中三维重建需要先将双侧配准的点云数据中的从场景中分割出来，并将牲畜按照统一的方向放置于相同的坐标系统中，因此在进行三维重建前，需要将数据进行姿势归一化处理，以下给出姿势归一化方法。
软件处理方法（自主研发）
软件实现方法论文参考：
@article{GUO201760,
title = "LSSA_CAU: An interactive 3d point clouds analysis software for body measurement of livestock with similar forms of cows or pigs",
journal = "Computers and Electronics in Agriculture",
volume = "138",
pages = "60 - 68",
year = "2017",
issn = "0168-1699",
doi = "https://doi.org/10.1016/j.compag.2017.04.014",
url = "http://www.sciencedirect.com/science/article/pii/S0168169917301746",
}
@article{GUO201959,
title = "A bilateral symmetry based pose normalization framework applied to livestock body measurement in point clouds",
journal = "Computers and Electronics in Agriculture",
volume = "160",
pages = "59 - 70",
year = "2019",
issn = "0168-1699",
doi = "https://doi.org/10.1016/j.compag.2019.03.010",
url = "http://www.sciencedirect.com/science/article/pii/S0168169918314893",
}
使用软件处理双侧点云数据，具体操作方法将在视频中给出：
处理结果：
五、三维重建
本文参考Leonid Pishchulinp论文[Building Statistical Shape Spaces for 3D Human Modeling]的方法，使用基于模板的非刚配准来实现三维重建。该方法包括初始化和非刚配准两个步骤。本项目在原有算法的基础上，通过修改其中算法参数和模型，将原有算法从人体转移到牲畜上，并达到一定的精度。
数据准备
模板数据：普通猪的网格数据
扫描数据：第四章获取的归一化后猪的点云数据
另外还需要选取一些特征点，使配准效果更好。因此，我们根据农业上猪的体尺测量，在模板数据和扫描数据上分别选取了37个对应的特征点。
初始化
为了使非刚配准成功进行，模板数据需要和扫描数据预对齐。这里使用对应的特征点集基于Horn等人提出的单位四元数法，实现模板数据和扫描数据间的刚性配准。
非刚配准
在变换中，对于模板数据的每个点均有一个4x4的变换矩阵。计算目标即找到这一系列变换矩阵使得模板数据中的顶点尽可能地变换到扫描数据中的对应点上，更好地匹配扫描数据。其中对应点寻找基于K最邻近算法实现，并且考虑两个控制条件：距离<50mm、法线夹角<60°。
结果评价
非刚配准结果与原始点云间的比较。
（1）定量评价
（2）定性评价
六、Demo演示
视频详情可见如下链接:：
牲畜体表信息的三维重建
想要了解该文章的更具体详情，可点击：
【3D视觉创新应用竞赛作品系列】牲畜体表信息的三维重建跳转阅读原文~
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