Import Reconstruction

Goal

In this tutorial you will learn how to import a reconstruction from a given file obtained with Bundler [1]:

• Load a file containing a set of cameras and 3D points.
• Show obtained results using Viz.

Code

#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"
      << " This program shows how to import a reconstructed scene in the \n"
      << " OpenCV Structure From Motion (SFM) module.\n"
      << " Usage:\n"
      << "        example_sfm_import_reconstruction <path_to_file>\n"
      << " where: file_path is the absolute path file into your system which contains\n"
      << "        the reconstructed scene. \n"
      << "---------------------------------------------------------------------------\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(); // black by default



  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 << "Press 'q' to close each windows ... " << endl;

  window.spin();

  return 0;
}

Results

The following picture shows a reconstruction from la Sagrada Familia (BCN) using dataset [2].

[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