examples/simple_bundle_adjuster.cc

// Ceres Solver - A fast non-linear least squares minimizer
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// Author: keir@google.com (Keir Mierle)
//
// A minimal, self-contained bundle adjuster using Ceres, that reads
// files from University of Washington' Bundle Adjustment in the Large dataset:
// http://grail.cs.washington.edu/projects/bal
//
// This does not use the best configuration for solving; see the more involved
// bundle_adjuster.cc file for details.

#include <cmath>
#include <cstdio>
#include <iostream>

#include "ceres/ceres.h"
#include "ceres/rotation.h"

// Read a Bundle Adjustment in the Large dataset.
class BALProblem {
 public:
  ~BALProblem() {
    delete[] point_index_;
    delete[] camera_index_;
    delete[] observations_;
    delete[] parameters_;
  }

  int num_observations()       const { return num_observations_;               }
  const double* observations() const { return observations_;                   }
  double* mutable_cameras()          { return parameters_;                     }
  double* mutable_points()           { return parameters_  + 9 * num_cameras_; }

  double* mutable_camera_for_observation(int i) {
    return mutable_cameras() + camera_index_[i] * 9;
  }
  double* mutable_point_for_observation(int i) {
    return mutable_points() + point_index_[i] * 3;
  }

  bool LoadFile(const char* filename) {
    FILE* fptr = fopen(filename, "r");
    if (fptr == NULL) {
      return false;
    };

    FscanfOrDie(fptr, "%d", &num_cameras_);
    FscanfOrDie(fptr, "%d", &num_points_);
    FscanfOrDie(fptr, "%d", &num_observations_);

    point_index_ = new int[num_observations_];
    camera_index_ = new int[num_observations_];
    observations_ = new double[2 * num_observations_];

    num_parameters_ = 9 * num_cameras_ + 3 * num_points_;
    parameters_ = new double[num_parameters_];

    for (int i = 0; i < num_observations_; ++i) {
      FscanfOrDie(fptr, "%d", camera_index_ + i);
      FscanfOrDie(fptr, "%d", point_index_ + i);
      for (int j = 0; j < 2; ++j) {
        FscanfOrDie(fptr, "%lf", observations_ + 2*i + j);
      }
    }

    for (int i = 0; i < num_parameters_; ++i) {
      FscanfOrDie(fptr, "%lf", parameters_ + i);
    }
    return true;
  }

 private:
  template<typename T>
  void FscanfOrDie(FILE *fptr, const char *format, T *value) {
    int num_scanned = fscanf(fptr, format, value);
    if (num_scanned != 1) {
      LOG(FATAL) << "Invalid UW data file.";
    }
  }

  int num_cameras_;
  int num_points_;
  int num_observations_;
  int num_parameters_;

  int* point_index_;
  int* camera_index_;
  double* observations_;
  double* parameters_;
};

// Templated pinhole camera model for used with Ceres.  The camera is
// parameterized using 9 parameters: 3 for rotation, 3 for translation, 1 for
// focal length and 2 for radial distortion. The principal point is not modeled
// (i.e. it is assumed be located at the image center).
struct SnavelyReprojectionError {
  SnavelyReprojectionError(double observed_x, double observed_y)
      : observed_x(observed_x), observed_y(observed_y) {}

  template <typename T>
  bool operator()(const T* const camera,
                  const T* const point,
                  T* residuals) const {
    // camera[0,1,2] are the angle-axis rotation.
    T p[3];
    ceres::AngleAxisRotatePoint(camera, point, p);

    // camera[3,4,5] are the translation.
    p[0] += camera[3];
    p[1] += camera[4];
    p[2] += camera[5];

    // Compute the center of distortion. The sign change comes from
    // the camera model that Noah Snavely's Bundler assumes, whereby
    // the camera coordinate system has a negative z axis.
    const T& focal = camera[6];
    T xp = - focal * p[0] / p[2];
    T yp = - focal * p[1] / p[2];

    // Apply second and fourth order radial distortion.
    const T& l1 = camera[7];
    const T& l2 = camera[8];
    T r2 = xp*xp + yp*yp;
    T distortion = T(1.0) + r2  * (l1 + l2  * r2);

    // Compute final projected point position.
    T predicted_x = distortion * xp;
    T predicted_y = distortion * yp;

    // The error is the difference between the predicted and observed position.
    residuals[0] = predicted_x - T(observed_x);
    residuals[1] = predicted_y - T(observed_y);

    return true;
  }

  double observed_x;
  double observed_y;
};

int main(int argc, char** argv) {
  google::InitGoogleLogging(argv[0]);
  if (argc != 2) {
    std::cerr << "usage: simple_bundle_adjuster <bal_problem>\n";
    return 1;
  }

  BALProblem bal_problem;
  if (!bal_problem.LoadFile(argv[1])) {
    std::cerr << "ERROR: unable to open file " << argv[1] << "\n";
    return 1;
  }

  // Create residuals for each observation in the bundle adjustment problem. The
  // parameters for cameras and points are added automatically.
  ceres::Problem problem;
  for (int i = 0; i < bal_problem.num_observations(); ++i) {
    // Each Residual block takes a point and a camera as input and outputs a 2
    // dimensional residual. Internally, the cost function stores the observed
    // image location and compares the reprojection against the observation.
    ceres::CostFunction* cost_function =
        new ceres::AutoDiffCostFunction<SnavelyReprojectionError, 2, 9, 3>(
            new SnavelyReprojectionError(
                bal_problem.observations()[2 * i + 0],
                bal_problem.observations()[2 * i + 1]));

    problem.AddResidualBlock(cost_function,
                             NULL /* squared loss */,
                             bal_problem.mutable_camera_for_observation(i),
                             bal_problem.mutable_point_for_observation(i));
  }

  // Make Ceres automatically detect the bundle structure. Note that the
  // standard solver, SPARSE_NORMAL_CHOLESKY, also works fine but it is slower
  // for standard bundle adjustment problems.
  ceres::Solver::Options options;
  options.linear_solver_type = ceres::DENSE_SCHUR;
  options.ordering_type = ceres::SCHUR;
  options.minimizer_progress_to_stdout = true;

  ceres::Solver::Summary summary;
  ceres::Solve(options, &problem, &summary);
  std::cout << summary.FullReport() << "\n";
  return 0;
}