本篇文章主要给出使用opencv sgbm重建三维点云的代码，鉴于自身水平所限，如有错误，欢迎批评指正。
环境：vs2015 ，opencv3.4.6，pcl1.8.0
原始数据使用D455采集，图像已做完立体校正，如下图所示（欢迎进Q群交流：874653199）：
左图：
右图：
视差结果图：
彩色视差结果图：
点云结果：
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#include <iostream>
#include <fstream>#include <opencv2/opencv.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>#include<pcl/io/ply_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>#define isStereoRectify void visualize(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud)
{pcl::visualization::PCLVisualizer viewer("3D Viewer");pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> src_h(cloud, 255, 255, 255);viewer.setBackgroundColor(0, 0, 0);viewer.addPointCloud(cloud, src_h, "cloud");while (!viewer.wasStopped()){viewer.spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}}void recon3d(cv::Mat disparty, double f, double cx, double cy, double baseline) {pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>());pcl::PointXYZ singlePoint;for (int i = 0; i < disparty.rows; i++) {for (int j = 0; j < disparty.cols; j++) {const double disp = disparty.at<float>(i, j);if (disp == 0) {continue;}else {singlePoint.z = f*baseline / disp;singlePoint.x = (i - cx) / f *singlePoint.z;singlePoint.y = (j - cy) / f *singlePoint.z;if (singlePoint.z >= -0.65 && singlePoint.z <= 0.3) {cloud->points.emplace_back(singlePoint);}}}}visualize(cloud);pcl::io::savePLYFileBinary("cloud.ply", *cloud);}int main(){cv::Mat imageL = cv::imread("E:/2_光学测量/6_数据/6_stereo/l0.jpg",0);cv::Mat imageR = cv::imread("E:/2_光学测量/6_数据/6_stereo/r0.jpg", 0);cv::Mat cameraMatrixL = (cv::Mat_<double>(3, 3) << 428.406, 0.000000, 420.335, 0.000000, 428.406, 238.037, 0.000000, 0.000000, 1.000000);cv::Mat distCoeffL = (cv::Mat_<double>(5, 1) << 0, 0, 0, 0, 0);cv::Mat cameraMatrixR = (cv::Mat_<double>(3, 3) << 428.406, 0.000000, 420.335, 0.000000, 428.406, 238.037, 0.000000, 0.000000, 1.000000);cv::Mat distCoeffR = (cv::Mat_<double>(5, 1) << 0, 0, 0, 0, 0);cv::Mat R = (cv::Mat_<double>(3, 3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);cv::Mat T = (cv::Mat_<double>(3, 1) << -0.0949472, 0, 0);#ifdef isStereoRectifycv::Mat Rl, Rr, Pl, Pr, Q;cv::Rect validROIL, validROIR;cv::Size imageSize = imageL.size();cv::stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q, cv::CALIB_ZERO_DISPARITY,0, imageSize, &validROIL, &validROIR);cv::Mat mapLx, mapLy, mapRx, mapRy;cv::initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pl, imageSize, CV_32FC1, mapLx, mapLy);cv::initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);cv::Mat rectifyImageL, rectifyImageR;cv::remap(imageL, rectifyImageL, mapLx, mapLy, cv::INTER_LINEAR);cv::remap(imageR, rectifyImageR, mapRx, mapRy, cv::INTER_LINEAR);imageL = rectifyImageL;imageR = rectifyImageR;#endif // sterocv::namedWindow("disparity", CV_WINDOW_NORMAL);int SADWindowSize =5, numberOfDisparities = 128;cv::Ptr<cv::StereoSGBM> sgbm = cv::StereoSGBM::create(0, numberOfDisparities, SADWindowSize);sgbm->setPreFilterCap(64);sgbm->setBlockSize(SADWindowSize);sgbm->setP1(8 * SADWindowSize* SADWindowSize);sgbm->setP2(64 * SADWindowSize* SADWindowSize);sgbm->setMinDisparity(0);sgbm->setNumDisparities(numberOfDisparities);sgbm->setUniquenessRatio(10);sgbm->setSpeckleWindowSize(200);sgbm->setSpeckleRange(64);sgbm->setDisp12MaxDiff(1);sgbm->setMode(cv::StereoSGBM::MODE_SGBM);cv::Mat disp, disp8, dispf;sgbm->compute(imageL, imageR, disp);disp.convertTo(disp, CV_32F, 1.0 / 16.0);//1.0/16.0 disp.convertTo(disp8, CV_8U, 1.0);imshow("disparity", disp8);cv::imwrite("disp_mono.png", disp8);cv::Mat disp8_color;cv::applyColorMap(disp8, disp8_color, cv::COLORMAP_JET);imshow("disparity_color", disp8_color);cv::imwrite("disp_color.png", disp8_color);recon3d(disp, cameraMatrixL.at<double>(0,0), cameraMatrixL.at<double>(0, 2), cameraMatrixL.at<double>(1, 2), T.at<double>(0));cv::waitKey(0);return 0;}
