前兩張圖采用本質矩陣計算RT的方法，然后三角測量計算三維坐標。具體原理可以百度。

后面的增量圖采用PnP的方法。通過計算圖2圖3匹配點和圖1圖2匹配點的公共部分，而圖1和圖2已經重建完成了，這些公共部分都是有點云坐標的，則直接PnP求解RT。

#pragma once #include <opencv2xfeatures2dnonfree.hpp> #include <opencv2features2dfeatures2d.hpp> #include <opencv2highguihighgui.hpp> #include <opencv2calib3dcalib3d.hpp> #include <fstream> //#include <opencv2> #include <iostream> #include <vector>using namespace std; using namespace cv; class ImageBag { public:ImageBag(Mat, Mat);~ImageBag();void SetKeyPoints(vector<KeyPoint>, Mat);void SetStructIdx(vector<int>);//公用參數Mat Image;Mat K;vector<KeyPoint> Key_points;Mat Descriptor;Mat R;Mat T;//特征點所對應的空間點在點云中的索引vector<int> Correspond_struct_idx; private:};ImageBag::ImageBag(Mat image, Mat k) {Image = image;K = k; }ImageBag::~ImageBag() {if (!Image.empty()){Image.release();}if (!K.empty()){K.release();} } void ImageBag::SetStructIdx(vector<int> index) {Correspond_struct_idx = index; } void ImageBag::SetKeyPoints(vector<KeyPoint> keyPoints, Mat descriptor) {Key_points = keyPoints;Descriptor = descriptor;//根據keyPoints來初始化Correspond_struct_idxfor (int i = 0; i < keyPoints.size(); i++){Correspond_struct_idx.push_back(-1);}}

以上是每一張圖的實體類，包含了每一個圖的一些信息。后面代碼寫起來會比較清晰。

#pragma once #include <opencv2xfeatures2dnonfree.hpp> #include <opencv2features2dfeatures2d.hpp> #include <opencv2highguihighgui.hpp> #include <opencv2calib3dcalib3d.hpp> #include <fstream> #include "ImageBag.h" //#include <opencv2> #include <iostream> using namespace cv; using namespace std; static class _3DBuilder { public:_3DBuilder();~_3DBuilder();static void ExtractFeatures(vector<ImageBag>&);static void MatchFeatures(Mat, Mat, vector<DMatch>&);static void GetMatchedPoints(vector<KeyPoint>, vector<KeyPoint>, vector<DMatch>, vector<Point2f>&, vector<Point2f>&);static bool FindTransform(Mat, vector<Point2f>, vector<Point2f>, Mat&, Mat&, Mat&);static void MaskoutPoints(vector<Point2f>&, Mat&);static void Reconstruct(Mat&, Mat&, Mat&, Mat&, Mat&, vector<Point2f>&, vector<Point2f>&, vector<Point3f>&); private:};_3DBuilder::_3DBuilder() { }_3DBuilder::~_3DBuilder() { }#pragma region 方法//檢測特征點 void _3DBuilder::ExtractFeatures(vector<ImageBag>& imageBagVector) {//讀取圖像，獲取圖像特征點，并保存//Ptr<SIFT> ss = siftdtc.create(0, 3, 0.04, 10);for (int i = 0;i<imageBagVector.size();i++){Mat image = imageBagVector[i].Image;if (image.empty()){continue;}vector<KeyPoint> key_points;Mat descriptor;//偶爾出現內存分配失敗的錯誤//Ptr <SURF> detector = SIFT::create();Ptr <SIFT> detector = SIFT::create();detector->detectAndCompute(image, noArray(), key_points, descriptor);//特征點過少，則排除該圖像if (key_points.size() <= 10){continue;}imageBagVector[i].SetKeyPoints(key_points, descriptor);} }void _3DBuilder::MatchFeatures(Mat query, Mat train, vector<DMatch>& matches) {vector<vector<DMatch>> knn_matches;BFMatcher matcher(NORM_L2);matcher.knnMatch(query, train, knn_matches, 2);//獲取滿足Ratio Test的最小匹配的距離float K = 0.6;float min_dist = FLT_MAX;for (int r = 0; r < knn_matches.size(); ++r){//Ratio Testif (knn_matches[r][0].distance > K*knn_matches[r][1].distance)continue;float dist = knn_matches[r][0].distance;if (dist < min_dist) min_dist = dist;}matches.clear();for (size_t r = 0; r < knn_matches.size(); ++r){//排除不滿足Ratio Test的點和匹配距離過大的點if (knn_matches[r][0].distance > K*knn_matches[r][1].distance ||knn_matches[r][0].distance > 5 * max(min_dist, 10.0f))continue;//保存匹配點matches.push_back(knn_matches[r][0]);} }//取matches中匹配上的點 void _3DBuilder::GetMatchedPoints(vector<KeyPoint> p1,vector<KeyPoint> p2,vector<DMatch> matches,vector<Point2f>& out_p1,vector<Point2f>& out_p2 ) {out_p1.clear();out_p2.clear();for (int i = 0; i < matches.size(); ++i){out_p1.push_back(p1[matches[i].queryIdx].pt);out_p2.push_back(p2[matches[i].trainIdx].pt);} }//取RT bool _3DBuilder::FindTransform(Mat K, vector<Point2f> p1, vector<Point2f> p2, Mat& R, Mat& T, Mat& mask) {//根據內參矩陣獲取相機的焦距和光心坐標（主點坐標）double focal_length = 0.5*(K.at<double>(0) + K.at<double>(4));Point2d principle_point(K.at<double>(2), K.at<double>(5));//根據匹配點求取本征矩陣，使用RANSAC，進一步排除失配點Mat E = findEssentialMat(p1, p2, focal_length, principle_point, RANSAC, 0.999, 1.0, mask);if (E.empty()) return false;double feasible_count = countNonZero(mask);cout << (int)feasible_count << " -in- " << p1.size() << endl;//對于RANSAC而言，outlier數量大于50%時，結果是不可靠的if (feasible_count <= 15 || (feasible_count / p1.size()) < 0.6)return false;//分解本征矩陣，獲取相對變換int pass_count = recoverPose(E, p1, p2, R, T, focal_length, principle_point, mask);//同時位于兩個相機前方的點的數量要足夠大if (((double)pass_count) / feasible_count < 0.7)return false;return true; }void _3DBuilder::MaskoutPoints(vector<Point2f>& p1, Mat& mask) {vector<Point2f> p1_copy = p1;p1.clear();for (int i = 0; i < mask.rows; ++i){if (mask.at<uchar>(i) > 0)p1.push_back(p1_copy[i]);} }void _3DBuilder::Reconstruct(Mat& K, Mat& R0, Mat& T0, Mat& R, Mat& T, vector<Point2f>& p1, vector<Point2f>& p2, vector<Point3f>& xyz) {//兩個相機的投影矩陣[R T]，triangulatePoints只支持float型//兩個相機的投影矩陣[R T]，triangulatePoints只支持float型Mat proj1(3, 4, CV_32FC1);Mat proj2(3, 4, CV_32FC1);R0.convertTo(proj1(Range(0, 3), Range(0, 3)), CV_32FC1);T0.convertTo(proj1.col(3), CV_32FC1);R.convertTo(proj2(Range(0, 3), Range(0, 3)), CV_32FC1);T.convertTo(proj2.col(3), CV_32FC1);Mat fK;K.convertTo(fK, CV_32FC1);proj1 = fK*proj1;proj2 = fK*proj2;//三角重建Mat s;triangulatePoints(proj1, proj2, p1, p2, s);xyz.clear();xyz.reserve(s.cols);for (int i = 0; i < s.cols; ++i){Mat_<float> col = s.col(i);col /= col(3); //齊次坐標，需要除以最后一個元素才是真正的坐標值xyz.push_back(Point3f(col(0), col(1), col(2)));}}void get_objpoints_and_imgpoints(vector<DMatch>& matches,vector<int>& struct_indices,vector<Point3f>& structure,vector<KeyPoint>& key_points,vector<Point3f>& object_points,vector<Point2f>& image_points) {object_points.clear();image_points.clear();for (int i = 0; i < matches.size(); ++i){int query_idx = matches[i].queryIdx;int train_idx = matches[i].trainIdx;int struct_idx = struct_indices[query_idx];if (struct_idx < 0) continue;object_points.push_back(structure[struct_idx]);image_points.push_back(key_points[train_idx].pt);} }#pragma endregion

上面是方法類。

#include <opencv2xfeatures2dnonfree.hpp> #include <opencv2features2dfeatures2d.hpp> #include <opencv2highguihighgui.hpp> #include <opencv2calib3dcalib3d.hpp> #include <fstream> #include "ImageBag.h" #include "3DBuilder.h" //#include <opencv2> #include <iostream> using namespace cv; using namespace std;void main() {vector<Point3f> All_structure;vector<Point3f> All_structureRGB;ofstream outfile("E:a學習資源與代碼三維重建SFM3DReconstructCPP - 多視角x64DebugXYZ.txt", ios::trunc);string imagePath = "E:a學習資源與代碼三維重建SFM3DReconstructCPP - 多視角x64DebugImage";//內參矩陣/*Mat K(Matx33d(2759.48, 0, 1520.69,0, 2764.16, 1006.81,0, 0, 1));*/Mat K(Matx33d(1520.4, 0.000000, 302.320000,0.000000, 1525.900000, 246.870000,0, 0, 1));//讀圖vector<ImageBag> ImageBags;int imageCount = 0;while (true){string imageFile = imagePath + to_string(imageCount)+".png";struct stat buffer;//判斷圖片是否存在if (stat(imageFile.c_str(), &buffer) != 0){break;}Mat image = imread(imageFile);ImageBag imageBag = ImageBag(image, K);ImageBags.push_back(imageBag);imageCount++;}//提取特征_3DBuilder::ExtractFeatures(ImageBags);for (int i = 0; i < ImageBags.size(); i++){for (int j = i+1; j < ImageBags.size(); j++){//兩兩開始比較vector<DMatch> matches;//特征匹配_3DBuilder::MatchFeatures(ImageBags[i].Descriptor, ImageBags[j].Descriptor, matches);if (matches.size() < 10){//匹配的點數過少continue;}if (i == 0){vector<Point2f> p1, p2;Mat R, T; //旋轉矩陣和平移向量Mat mask; //mask中大于零的點代表匹配點，等于零代表失配點_3DBuilder::GetMatchedPoints(ImageBags[i].Key_points, ImageBags[j].Key_points, matches, p1, p2);_3DBuilder::FindTransform(K, p1, p2, R, T, mask);if (R.dims == 0){continue;}ImageBags[j].R = R;ImageBags[j].T = T;_3DBuilder::MaskoutPoints(p1, mask);_3DBuilder::MaskoutPoints(p2, mask);if (p1.size() == 0){continue;}vector<Point3f> xyz;Mat R0 = Mat::eye(3, 3, CV_64FC1);Mat T0 = Mat::zeros(3, 1, CV_64FC1);_3DBuilder::Reconstruct(K, R0, T0, R, T, p1, p2, xyz);//根據mask來記錄各個點和點云的關系。int idx = 0;for (int r = 0; r < matches.size(); r++){if (mask.at<uchar>(r) > 0){ImageBags[i].Correspond_struct_idx[matches[r].queryIdx] = All_structure.size() + idx;ImageBags[j].Correspond_struct_idx[matches[r].trainIdx] = All_structure.size() + idx;idx++;}}//將xyz記錄進All_structurefor (int s = 0; s < xyz.size(); s++){All_structure.push_back(xyz[s]);}}else {//不是0號圖的話 就要使用PnP方法，進行像素點到世界點的匹配//先找出對應點vector<Point2f> p1, p2;Mat R, T; //旋轉矩陣和平移向量_3DBuilder::GetMatchedPoints(ImageBags[i].Key_points, ImageBags[j].Key_points, matches, p1, p2);//然后遍歷所有的match點，看那些已經有世界坐標了vector<int> pointWorldPositionIndex;vector<int> pointImagePositionIndex;for (int k = 0; k < matches.size(); k++){if (ImageBags[i].Correspond_struct_idx[matches[k].queryIdx] > 0){pointWorldPositionIndex.push_back(ImageBags[i].Correspond_struct_idx[matches[k].queryIdx]);pointImagePositionIndex.push_back(matches[k].trainIdx);}}if (pointWorldPositionIndex.size() <= 8){//對應點過少 無法求解continue;}//vector<Point3f> object_points;vector<Point2f> image_points;for (int k = 0; k < pointWorldPositionIndex.size(); k++){object_points.push_back(All_structure[pointWorldPositionIndex[k]]);image_points.push_back(ImageBags[j].Key_points[pointImagePositionIndex[k]].pt);}solvePnPRansac(object_points, image_points, K, noArray(), R, T);//將旋轉向量轉換為旋轉矩陣Rodrigues(R, R);vector<Point3f> next_structure;_3DBuilder::Reconstruct(K, ImageBags[i].R, ImageBags[i].T, R, T, p1, p2, next_structure);ImageBags[j].R = R;ImageBags[j].T = T;//根據mask來記錄各個點和點云的關系。int idx = 0;for (int r = 0; r < matches.size(); r++){ImageBags[i].Correspond_struct_idx[matches[r].queryIdx] = All_structure.size() + idx;ImageBags[j].Correspond_struct_idx[matches[r].trainIdx] = All_structure.size() + idx;idx++;}for (int k = 0; k < next_structure.size();k++){All_structure.push_back(next_structure[k]);}//}}}/*ofstream outfile;outfile.open(".XYZ.txt", ios::binary | ios::app | ios::in | ios::out);*/for (int k = 0; k < All_structure.size(); k++){outfile << All_structure[k].x << " "<< All_structure[k].y<<" "<< All_structure[k].z;outfile << "n";}outfile.close();}

主函數。內參已經在程序中寫出了。

使用的圖如下：

重建后的效果：

知乎視頻?www.zhihu.com

附件一直傳不了，不知道是不是bug了。需要原圖的私聊吧！

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