OpenCV实现SfM(二):多目三维重建

上一次学习了双目三维重建,这次来学习基于多目的三维重建。首先,为了简化问题,我们要做一个重要假设:用于多目重建的图像是有序的,即相邻图像的拍摄位置也是相邻的


求第三个相机的变换矩阵

由前面的文章我们知道,两个相机之间的变换矩阵可以通过findEssentialMat以及recoverPose函数来实现,设第一个相机的坐标系为世界坐标系,现在加入第三幅图像(相机),如何确定第三个相机(后面称为相机三)到到世界坐标系的变换矩阵呢?

最简单的想法,就是沿用双目重建的方法,即在第三幅图像和第一幅图像之间提取特征点,然后调用findEssentialMat和recoverPose。那么加入第四幅、第五幅,乃至更多呢?随着图像数量的增加,新加入的图像与第一幅图像的差异可能越来越大,特征点的提取变得异常困难,这时就不能再沿用双目重建的方法了。

那么能不能用新加入的图像和相邻图像进行特征匹配呢?比如第三幅与第二幅匹配,第四幅与第三幅匹配,以此类推。当然可以,但是这时就不能继续使用findEssentialMat和recoverPose来求取相机的变换矩阵了,因为这两个函数求取的是相对变换,比如相机三到相机二的变换,而我们需要的是相机三到相机一的变换。有人说,既然知道相机二到相机一的变换,又知道相机到三到相机二的变换,不就能求出相机三到相机一的变换吗?实际上,通过这种方式,你只能求出相机三到相机一的旋转变换(旋转矩阵R),而他们之间的位移向量T,是无法求出的。这是因为上面两个函数求出的位移向量,都是单位向量,丢失了相机之间位移的比例关系。

说了这么多,我们要怎么解决这些问题?现在请出本文的主角——solvePnP和solvePnPRansac。根据opencv的官方解释,该函数根据空间中的点与图像中的点的对应关系,求解相机在空间中的位置。也就是说,我知道一些空间当中点的坐标,还知道这些点在图像中的像素坐标,那么solvePnP就可以告诉我相机在空间当中的坐标。solvePnP和solvePnPRansac所实现的功能相同,只不过后者使用了随机一致性采样,使其对噪声更鲁棒,本文使用后者。

好了,有这么好的函数,怎么用于我们的三维重建呢?首先,使用双目重建的方法,对头两幅图像进行重建,这样就得到了一些空间中的点,加入第三幅图像后,使其与第二幅图像进行特征匹配,这些匹配点中,肯定有一部分也是图像二与图像一之间的匹配点,也就是说,这些匹配点中有一部分的空间坐标是已知的,同时又知道这些点在第三幅图像中的像素坐标,嗯,solvePnP所需的信息都有了,自然第三个相机的空间位置就求出来了。由于空间点的坐标都是世界坐标系下的(即第一个相机的坐标系),所以由solvePnP求出的相机位置也是世界坐标系下的,即相机三到相机一的变换矩阵。

加入更多图像

通过上面的方法得到相机三的变换矩阵后,就可以使用上一篇文章提到的triangulatePoints方法将图像三和图像二之间的匹配点三角化,得到其空间坐标。为了使之后的图像仍能使用以上方法求解变换矩阵,我们还需要将新得到的空间点和之前的三维点云融合。已经存在的空间点,就没必要再添加了,只添加在图像二和三之间匹配,但在图像一和图像三中没有匹配的点。如此反复。 
多目重建流程 
为了方便点云的融合以及今后的扩展,我们需要存储图像中每个特征点在空间中的对应点。在代码中使用了一个二维列表,名字为correspond_struct_idx,correspond_struct_idx[i][j]代表第i幅图像第j个特征点所对应的空间点在点云中的索引,若索引小于零,说明该特征点在空间当中没有对应点。通过此结构,由特征匹配中的queryIdx和trainIdx就可以查询某个特征点在空间中的位置。

代码实现

前一篇文章的很多代码不用修改,还可以继续使用,但是程序的流程有了较大变化。首先是初始化点云,也就是通过双目重建方法对图像序列的头两幅图像进行重建,并初始化correspond_struct_idx。

void init_structure(
    Mat K,
    vector<vector>& key_points_for_all, 
    vector<vector>& colors_for_all,
    vector<vector>& matches_for_all,
    vector& structure,
    vector<vector<int>>& correspond_struct_idx,
    vector& colors,
    vector& rotations,
    vector& motions
    )
{
    //计算头两幅图像之间的变换矩阵
    vector p1, p2;
    vector c2;
    Mat R, T;   //旋转矩阵和平移向量
    Mat mask;   //mask中大于零的点代表匹配点,等于零代表失配点
    get_matched_points(key_points_for_all[0], key_points_for_all[1], matches_for_all[0], p1, p2);
    get_matched_colors(colors_for_all[0], colors_for_all[1], matches_for_all[0], colors, c2);
    find_transform(K, p1, p2, R, T, mask);

    //对头两幅图像进行三维重建
    maskout_points(p1, mask);
    maskout_points(p2, mask);
    maskout_colors(colors, mask);

    Mat R0 = Mat::eye(3, 3, CV_64FC1);
    Mat T0 = Mat::zeros(3, 1, CV_64FC1);
    reconstruct(K, R0, T0, R, T, p1, p2, structure);
    //保存变换矩阵
    rotations = { R0, R };
    motions = { T0, T };

    //将correspond_struct_idx的大小初始化为与key_points_for_all完全一致
    correspond_struct_idx.clear();
    correspond_struct_idx.resize(key_points_for_all.size());
    for (int i = 0; i < key_points_for_all.size(); ++i)
    {
        correspond_struct_idx[i].resize(key_points_for_all[i].size(), -1);
    }

    //填写头两幅图像的结构索引
    int idx = 0;
    vector& matches = matches_for_all[0];
    for (int i = 0; i < matches.size(); ++i)
    {
        if (mask.at(i) == 0)
            continue;

        correspond_struct_idx[0][matches[i].queryIdx] = idx;
        correspond_struct_idx[1][matches[i].trainIdx] = idx;
        ++idx;
    }
}
 
   
   
   
   
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初始点云得到后,就可以使用增量方式重建剩余图像,注意,在代码中为了方便实现,所有图像之间的特征匹配已经事先完成了,并保存在matches_for_all这个列表中。增量重建的关键是调用solvePnPRansac,而这个函数需要空间点坐标和对应的像素坐标作为参数,有了correspond_struct_idx,实现这个对应关系的查找还是很方便的,如下。

void get_objpoints_and_imgpoints(
    vector& matches,
    vector<int>& struct_indices, 
    vector& structure, 
    vector& key_points,
    vector& object_points,
    vector& 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);
    }
}
 
   
   
   
   
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之后调用solvePnPRansac得到相机的旋转向量和位移,由于我们使用的都是旋转矩阵,所以这里要调用opencv的Rodrigues函数将旋转向量变换为旋转矩阵。之后,使用上一篇文章中用到的reconstruct函数对匹配点进行重建(三角化),不过为了适用于多目重建,做了一些简单修改。

void reconstruct(Mat& K, Mat& R1, Mat& T1, Mat& R2, Mat& T2, vector& p1, vector& p2, vector& structure)
{
    //两个相机的投影矩阵[R T],triangulatePoints只支持float型
    Mat proj1(3, 4, CV_32FC1);
    Mat proj2(3, 4, CV_32FC1);

    R1.convertTo(proj1(Range(0, 3), Range(0, 3)), CV_32FC1);
    T1.convertTo(proj1.col(3), CV_32FC1);

    R2.convertTo(proj2(Range(0, 3), Range(0, 3)), CV_32FC1);
    T2.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);

    structure.clear();
    structure.reserve(s.cols);
    for (int i = 0; i < s.cols; ++i)
    {
        Mat_<float> col = s.col(i);
        col /= col(3);  //齐次坐标,需要除以最后一个元素才是真正的坐标值
        structure.push_back(Point3f(col(0), col(1), col(2)));
    }
}
 
   
   
   
   
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最后,将重建结构与之前的点云进行融合。

void fusion_structure(
    vector& matches, 
    vector<int>& struct_indices, 
    vector<int>& next_struct_indices,
    vector& structure, 
    vector& next_structure,
    vector& colors,
    vector& next_colors
    )
{
    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) //若该点在空间中已经存在,则这对匹配点对应的空间点应该是同一个,索引要相同
        {
            next_struct_indices[train_idx] = struct_idx;
            continue;
        }

        //若该点在空间中已经存在,将该点加入到结构中,且这对匹配点的空间点索引都为新加入的点的索引
        structure.push_back(next_structure[i]);
        colors.push_back(next_colors[i]);
        struct_indices[query_idx] = next_struct_indices[train_idx] = structure.size() - 1;
    }
}
 
   
   
   
   
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整个增量方式重建图像的代码大致如下。

//初始化结构(三维点云)
init_structure(
    K,
    key_points_for_all,
    colors_for_all,
    matches_for_all,
    structure,
    correspond_struct_idx,
    colors,
    rotations,
    motions
    );

//增量方式重建剩余的图像
for (int i = 1; i < matches_for_all.size(); ++i)
{
    vector object_points;
    vector image_points;
    Mat r, R, T;
    //Mat mask;

    //获取第i幅图像中匹配点对应的三维点,以及在第i+1幅图像中对应的像素点
    get_objpoints_and_imgpoints(
        matches_for_all[i], 
        correspond_struct_idx[i], 
        structure,
        key_points_for_all[i+1], 
        object_points,
        image_points
        );

    //求解变换矩阵
    solvePnPRansac(object_points, image_points, K, noArray(), r, T);
    //将旋转向量转换为旋转矩阵
    Rodrigues(r, R);
    //保存变换矩阵
    rotations.push_back(R);
    motions.push_back(T);

    vector p1, p2;
    vector c1, c2;
    get_matched_points(key_points_for_all[i], key_points_for_all[i + 1], matches_for_all[i], p1, p2);
    get_matched_colors(colors_for_all[i], colors_for_all[i + 1], matches_for_all[i], c1, c2);

    //根据之前求得的R,T进行三维重建
    vector next_structure;
    reconstruct(K, rotations[i], motions[i], R, T, p1, p2, next_structure);

    //将新的重建结果与之前的融合
    fusion_structure(
        matches_for_all[i], 
        correspond_struct_idx[i], 
        correspond_struct_idx[i + 1],
        structure, 
        next_structure,
        colors,
        c1
        );
}
 
   
   
   
   
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测试

我用了八幅图像进行测试,正如问题简化中所要求的那样,图像是有序的。 
图片序列 
程序的大部分时间花在特征提取和匹配上,真正的重建过程耗时很少。最终结果如下。 
重建结果 
图中每个彩色坐标系都代表一个相机。


思考

  • 这个多目三维重建程序,要求图像必须是有序的,如果图像无序,比如只是对某个目标在不同角度的随意拍摄,程序应该如何修改?
  • 增量式三维重建方法,有一个很大的缺点——随着图像的不断增加,误差会不断累积,最后误差过大以至于完全偏离重建的目标,怎么解决?

有兴趣的读者可以思考一下上面两个问题,第二个问题比较难,我会在下一篇文章中详细介绍。


程序

程序运行后会将三维重建的结果写入Viewer目录下的structure.yml文件中,在Viewer目录下有一个SfMViewer程序,直接运行即可读取yml文件并显示三维结构。

完整程序:

#include 
#include 
#include 
#include 
#include 
#include "tinydir.h"

using namespace cv;
using namespace std;
#define OUTPUT_NAME "Viewer/structure.yml"

void extract_features(
        vector& image_names,
        vector>& key_points_for_all,
        vector& descriptor_for_all,
        vector>& colors_for_all
        )
{
        key_points_for_all.clear();
        descriptor_for_all.clear();
        Mat image;

        Ptr sift = xfeatures2d::SIFT::create(0, 3, 0.04, 10);
        for (auto it = image_names.begin(); it != image_names.end(); ++it)
        {
                image = imread(*it);
                if (image.empty()) continue;
                cout << "Extracing features: " << *it << endl;
                vector key_points;
                Mat descriptor;

                sift->detectAndCompute(image, noArray(), key_points, descriptor);
                if (key_points.size() <= 10) continue;

                key_points_for_all.push_back(key_points);
                descriptor_for_all.push_back(descriptor);

                vector colors(key_points.size());
                for (int i = 0; i < key_points.size(); ++i)
                {
                        Point2f& p = key_points[i].pt;
                        colors[i] = image.at(p.y, p.x);
                }
                colors_for_all.push_back(colors);
        }
}
void match_features(Mat& query, Mat& train, vector& matches)
{
        vector> knn_matches;
        BFMatcher matcher(NORM_L2);
        matcher.knnMatch(query, train, knn_matches, 2);

        //获取满足Ratio Test的最小匹配的距离
        float min_dist = FLT_MAX;
        for (int r = 0; r < knn_matches.size(); ++r)
        {
                //Ratio Test
                if (knn_matches[r][0].distance > 0.6*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)
        {
                //排除不满足Ratio Test的点和匹配距离过大的点
                if (
                        knn_matches[r][0].distance > 0.6*knn_matches[r][1].distance ||
                        knn_matches[r][0].distance > 5 * max(min_dist, 10.0f)
                        )
                        continue;
                //保存匹配点
                matches.push_back(knn_matches[r][0]);
        }
}
void match_features(vector& descriptor_for_all, vector>& matches_for_all)
{
        matches_for_all.clear();
        // n个图像,两两顺次有 n-1 对匹配
        // 1与2匹配,2与3匹配,3与4匹配,以此类推
        for (int i = 0; i < descriptor_for_all.size() - 1; ++i)
        {
                cout << "Matching images " << i << " - " << i + 1 << endl;
                vector matches;
                match_features(descriptor_for_all[i], descriptor_for_all[i + 1], matches);
                matches_for_all.push_back(matches);
        }
}
bool find_transform(Mat& K, vector& p1, vector& p2, Mat& R, Mat& T, Mat& mask)
{
        //根据内参矩阵获取相机的焦距和光心坐标(主点坐标)
        double focal_length = 0.5*(K.at(0) + K.at(4));
        Point2d principle_point(K.at(2), K.at(5));

        //根据匹配点求取本征矩阵,使用RANSAC,进一步排除失配点
        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;
        //对于RANSAC而言,outlier数量大于50%时,结果是不可靠的
        if (feasible_count <= 15 || (feasible_count / p1.size()) < 0.6)
                return false;

        //分解本征矩阵,获取相对变换
        int pass_count = recoverPose(E, p1, p2, R, T, focal_length, principle_point, mask);

        //同时位于两个相机前方的点的数量要足够大
        if (((double)pass_count) / feasible_count < 0.7)
                return false;
        return true;
}
void get_matched_points(
        vector& p1,
        vector& p2,
        vector matches,
        vector& out_p1,
        vector& 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);
        }
}
void get_matched_colors(
        vector& c1,
        vector& c2,
        vector matches,
        vector& out_c1,
        vector& out_c2
        )
{
        out_c1.clear();
        out_c2.clear();
        for (int i = 0; i < matches.size(); ++i)
        {
                out_c1.push_back(c1[matches[i].queryIdx]);
                out_c2.push_back(c2[matches[i].trainIdx]);
        }
}
void reconstruct(Mat& K, Mat& R1, Mat& T1, Mat& R2, Mat& T2, vector& p1, vector& p2, vector& structure)
{
        //两个相机的投影矩阵[R T],triangulatePoints只支持float型
        Mat proj1(3, 4, CV_32FC1);
        Mat proj2(3, 4, CV_32FC1);

        R1.convertTo(proj1(Range(0, 3), Range(0, 3)), CV_32FC1);
        T1.convertTo(proj1.col(3), CV_32FC1);

        R2.convertTo(proj2(Range(0, 3), Range(0, 3)), CV_32FC1);
        T2.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);

        structure.clear();
        structure.reserve(s.cols);
        for (int i = 0; i < s.cols; ++i)
        {
                Mat_ col = s.col(i);
                col /= col(3);  //齐次坐标,需要除以最后一个元素才是真正的坐标值
                structure.push_back(Point3f(col(0), col(1), col(2)));
        }
}
void maskout_points(vector& p1, Mat& mask)
{
        vector p1_copy = p1;
        p1.clear();

        for (int i = 0; i < mask.rows; ++i)
        {
                if (mask.at(i) > 0)
                        p1.push_back(p1_copy[i]);
        }
}
void maskout_colors(vector& p1, Mat& mask)
{
        vector p1_copy = p1;
        p1.clear();
        for (int i = 0; i < mask.rows; ++i)
        {
                if (mask.at(i) > 0)
                        p1.push_back(p1_copy[i]);
        }
}
void save_structure(string file_name, vector& rotations, vector& motions, vector& structure, vector& colors)
{
        int n = (int)rotations.size();
        FileStorage fs(file_name, FileStorage::WRITE);
        fs << "Camera Count" << n;
        fs << "Point Count" << (int)structure.size();

        fs << "Rotations" << "[";
        for (size_t i = 0; i < n; ++i)
        {
                fs << rotations[i];
        }
        fs << "]";

        fs << "Motions" << "[";
        for (size_t i = 0; i < n; ++i)
        {
                fs << motions[i];
        }
        fs << "]";

        fs << "Points" << "[";
        for (size_t i = 0; i < structure.size(); ++i)
        {
                fs << structure[i];
        }
        fs << "]";

        fs << "Colors" << "[";
        for (size_t i = 0; i < colors.size(); ++i)
        {
                fs << colors[i];
        }
        fs << "]";

        fs.release();
}
void get_objpoints_and_imgpoints(
        vector& matches,
        vector& struct_indices,
        vector& structure,
        vector& key_points,
        vector& object_points,
        vector& 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);
        }
}
void fusion_structure(
        vector& matches,
        vector& struct_indices,
        vector& next_struct_indices,
        vector& structure,
        vector& next_structure,
        vector& colors,
        vector& next_colors
        )
{
        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) //若该点在空间中已经存在,则这对匹配点对应的空间点应该是同一个,索引要相同
                {
                        next_struct_indices[train_idx] = struct_idx;
                        continue;
                }
                //若该点在空间中已经存在,将该点加入到结构中,且这对匹配点的空间点索引都为新加入的点的索引
                structure.push_back(next_structure[i]);
                colors.push_back(next_colors[i]);
                struct_indices[query_idx] = next_struct_indices[train_idx] = structure.size() - 1;
        }
}
void init_structure(
        Mat K,
        vector>& key_points_for_all,
        vector>& colors_for_all,
        vector>& matches_for_all,
        vector& structure,
        vector>& correspond_struct_idx,
        vector& colors,
        vector& rotations,
        vector& motions
        )
{
        //计算头两幅图像之间的变换矩阵
        vector p1, p2;
        vector c2;
        Mat R, T;       //旋转矩阵和平移向量
        Mat mask;       //mask中大于零的点代表匹配点,等于零代表失配点
        get_matched_points(key_points_for_all[0], key_points_for_all[1], matches_for_all[0], p1, p2);
        get_matched_colors(colors_for_all[0], colors_for_all[1], matches_for_all[0], colors, c2);
        find_transform(K, p1, p2, R, T, mask);

        //对头两幅图像进行三维重建
        maskout_points(p1, mask);
        maskout_points(p2, mask);
        maskout_colors(colors, mask);

        Mat R0 = Mat::eye(3, 3, CV_64FC1);
        Mat T0 = Mat::zeros(3, 1, CV_64FC1);
        reconstruct(K, R0, T0, R, T, p1, p2, structure);
        //保存变换矩阵
        rotations = { R0, R };
        motions = { T0, T };

        //将correspond_struct_idx的大小初始化为与key_points_for_all完全一致
        correspond_struct_idx.clear();
        correspond_struct_idx.resize(key_points_for_all.size());
        for (int i = 0; i < key_points_for_all.size(); ++i)
        {
                correspond_struct_idx[i].resize(key_points_for_all[i].size(), -1);
        }

        //填写头两幅图像的结构索引
        int idx = 0;
        vector& matches = matches_for_all[0];
        for (int i = 0; i < matches.size(); ++i)
        {
                if (mask.at(i) == 0)
                        continue;

                correspond_struct_idx[0][matches[i].queryIdx] = idx;
                correspond_struct_idx[1][matches[i].trainIdx] = idx;
                ++idx;
        }
}
void get_file_names(string dir_name, vector & names)
{
        names.clear();
        tinydir_dir dir;
        tinydir_open(&dir, dir_name.c_str());

        while (dir.has_next)
        {
                tinydir_file file;
                tinydir_readfile(&dir, &file);
                if (!file.is_dir)
                {
            //cout< img_names;
        get_file_names("images", img_names);
    for (auto it = img_names.begin(); it != img_names.end(); ++it)
    {
         cout<<*it<> key_points_for_all;
        vector descriptor_for_all;
        vector> colors_for_all;
        vector> matches_for_all;

        extract_features(img_names, key_points_for_all, descriptor_for_all, colors_for_all);

        match_features(descriptor_for_all, matches_for_all);

        vector structure;
        vector> correspond_struct_idx;

   	vector colors;
        vector rotations;
        vector motions;
        init_structure(
                K,
                key_points_for_all,
                colors_for_all,
                matches_for_all,
                structure,
                correspond_struct_idx,
                colors,
                rotations,
                motions
                );

        //增量方式重建剩余的图像
        for (int i = 1; i < matches_for_all.size(); ++i)
        {
                vector object_points;
                vector image_points;
                Mat r, R, T;
                //Mat mask;

                //获取第i幅图像中匹配点对应的三维点,以及在第i+1幅图像中对应的像素点
                get_objpoints_and_imgpoints(
                        matches_for_all[i],
                        correspond_struct_idx[i],
                        structure,
                        key_points_for_all[i+1],
                        object_points,
                        image_points
                        );

                //求解变换矩阵
                solvePnPRansac(object_points, image_points, K, noArray(), r, T);
                //将旋转向量转换为旋转矩阵
                Rodrigues(r, R);
                //保存变换矩阵
                rotations.push_back(R);
                motions.push_back(T);

                vector p1, p2;
                vector c1, c2;
                get_matched_points(key_points_for_all[i], key_points_for_all[i + 1], matches_for_all[i], p1, p2);
                get_matched_colors(colors_for_all[i], colors_for_all[i + 1], matches_for_all[i], c1, c2);

                //根据之前求得的R,T进行三维重建
                vector next_structure;
                reconstruct(K, rotations[i], motions[i], R, T, p1, p2, next_structure);

                //将新的重建结果与之前的融合
                fusion_structure(
                        matches_for_all[i],
                        correspond_struct_idx[i],
                        correspond_struct_idx[i + 1],
                        structure,
                        next_structure,
                        colors,
                        c1
                        );
        }

        //保存
        save_structure(OUTPUT_NAME, rotations, motions, structure, colors);
    cout<
CXX ?= g++

CXXFLAGS += -c -std=c++11 -Wall $(shell pkg-config --cflags opencv)
LDFLAGS +=  -lopencv_xfeatures2d -lopencv_highgui -lopencv_core -lopencv_imgproc -lopencv_imgcodecs -lopencv_calib3d -lopencv_features2d

all: opencv_example

opencv_example: example.o; $(CXX) $< -o $@ $(LDFLAGS)

%.o: %.cpp; $(CXX) $< -o $@ $(CXXFLAGS)

clean: ; rm -f example.o opencv_example

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