导入重建

目标

在本教程中，您将学习如何从使用 Bundler [1] 获取的文件中导入重建。

• 加载包含一组相机和3D点的文件。
• 使用 Viz 显示获得的结果。

代码

#include <opencv2/sfm.hpp>
#include <opencv2/viz.hpp>
 
#include <iostream>
 
using namespace std;
using namespace cv;
using namespace cv::sfm;
 
static void help() {
cout
<< "\n---------------------------------------------------------------------------\n"
<< " 此程序展示了如何在 OpenCV 运动结构 (SFM) 模块中导入重建场景。\n"
<< " 使用方法：\n"
<< " example_sfm_import_reconstruction <path_to_file>\n"
<< " 其中：file_path 是包含重建场景的文件的绝对路径。 \n"
<< "---------------------------------------------------------------------------\n\n"
<< endl;
<< "---------------------------------------------------------------------------\n\n"
<< endl;
}
 
 
int main(int argc, char* argv[])
{
 
  if ( argc != 2 ) {
help();
exit(0);
  }
 
 
vector<Mat> Rs, Ts, Ks, points3d;
importReconstruction(argv[1], Rs, Ts, Ks, points3d, SFM_IO_BUNDLER);
 
 
 
  viz::Viz3d window("Coordinate Frame");
window.setWindowSize(Size(500,500));
window.setWindowPosition(Point(150,150));
window.setBackgroundColor(); // 默认为黑色
 
 
 
vector<Vec3d> point_cloud;
  for (int i = 0; i < points3d.size(); ++i){
point_cloud.push_back(Vec3f(points3d[i]));
  }
 
 
 
vector<Affine3d> path;
  for (size_t i = 0; i < Rs.size(); ++i)
path.push_back(Affine3d(Rs[i], Ts[i]));
 
 
 
  viz::WCloud cloud_widget(point_cloud, viz::Color::green());
  viz::WTrajectory trajectory(path, viz::WTrajectory::FRAMES, 0.5);
  viz::WTrajectoryFrustums frustums(path, Vec2f(0.889484, 0.523599), 0.5,
viz::Color::yellow());
 
window.showWidget("point_cloud", cloud_widget);
window.showWidget("cameras", trajectory);
window.showWidget("frustums", frustums);
 
 
cout << endl << "按 'q' 关闭每个窗口 ... " << endl;
 
window.spin();
 
  return 0;
}

结果

以下图片显示了使用数据集 [2] 对拉·萨格拉达·菲利亚（BCN）进行的重建。

[1] http://www.cs.cornell.edu/~snavely/bundler

[2] Penate Sanchez, A. and Moreno-Noguer, F. and Andrade Cetto, J. and Fleuret, F. (2014). LETHA: Learning from High Quality Inputs for 3D Pose Estimation in Low Quality Images. Proceedings of the International Conference on 3D vision (3DV). URL