219 lines
7.8 KiB
C++
219 lines
7.8 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: keir@google.com (Keir Mierle)
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//
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// A minimal, self-contained bundle adjuster using Ceres, that reads
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// files from University of Washington' Bundle Adjustment in the Large dataset:
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// http://grail.cs.washington.edu/projects/bal
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//
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// This does not use the best configuration for solving; see the more involved
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// bundle_adjuster.cc file for details.
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#include <cmath>
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#include <cstdio>
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#include <iostream>
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#include "ceres/ceres.h"
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#include "ceres/rotation.h"
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// Read a Bundle Adjustment in the Large dataset.
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class BALProblem {
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public:
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~BALProblem() {
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delete[] point_index_;
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delete[] camera_index_;
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delete[] observations_;
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delete[] parameters_;
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}
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int num_observations() const { return num_observations_; }
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const double* observations() const { return observations_; }
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double* mutable_cameras() { return parameters_; }
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double* mutable_points() { return parameters_ + 9 * num_cameras_; }
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double* mutable_camera_for_observation(int i) {
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return mutable_cameras() + camera_index_[i] * 9;
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}
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double* mutable_point_for_observation(int i) {
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return mutable_points() + point_index_[i] * 3;
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}
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bool LoadFile(const char* filename) {
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FILE* fptr = fopen(filename, "r");
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if (fptr == NULL) {
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return false;
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};
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FscanfOrDie(fptr, "%d", &num_cameras_);
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FscanfOrDie(fptr, "%d", &num_points_);
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FscanfOrDie(fptr, "%d", &num_observations_);
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point_index_ = new int[num_observations_];
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camera_index_ = new int[num_observations_];
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observations_ = new double[2 * num_observations_];
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num_parameters_ = 9 * num_cameras_ + 3 * num_points_;
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parameters_ = new double[num_parameters_];
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for (int i = 0; i < num_observations_; ++i) {
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FscanfOrDie(fptr, "%d", camera_index_ + i);
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FscanfOrDie(fptr, "%d", point_index_ + i);
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for (int j = 0; j < 2; ++j) {
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FscanfOrDie(fptr, "%lf", observations_ + 2*i + j);
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}
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}
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for (int i = 0; i < num_parameters_; ++i) {
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FscanfOrDie(fptr, "%lf", parameters_ + i);
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}
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return true;
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}
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private:
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template<typename T>
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void FscanfOrDie(FILE *fptr, const char *format, T *value) {
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int num_scanned = fscanf(fptr, format, value);
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if (num_scanned != 1) {
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LOG(FATAL) << "Invalid UW data file.";
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}
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}
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int num_cameras_;
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int num_points_;
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int num_observations_;
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int num_parameters_;
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int* point_index_;
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int* camera_index_;
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double* observations_;
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double* parameters_;
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};
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// Templated pinhole camera model for used with Ceres. The camera is
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// parameterized using 9 parameters: 3 for rotation, 3 for translation, 1 for
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// focal length and 2 for radial distortion. The principal point is not modeled
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// (i.e. it is assumed be located at the image center).
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struct SnavelyReprojectionError {
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SnavelyReprojectionError(double observed_x, double observed_y)
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: observed_x(observed_x), observed_y(observed_y) {}
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template <typename T>
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bool operator()(const T* const camera,
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const T* const point,
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T* residuals) const {
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// camera[0,1,2] are the angle-axis rotation.
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T p[3];
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ceres::AngleAxisRotatePoint(camera, point, p);
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// camera[3,4,5] are the translation.
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p[0] += camera[3];
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p[1] += camera[4];
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p[2] += camera[5];
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// Compute the center of distortion. The sign change comes from
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// the camera model that Noah Snavely's Bundler assumes, whereby
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// the camera coordinate system has a negative z axis.
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T xp = - p[0] / p[2];
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T yp = - p[1] / p[2];
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// Apply second and fourth order radial distortion.
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const T& l1 = camera[7];
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const T& l2 = camera[8];
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T r2 = xp*xp + yp*yp;
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T distortion = T(1.0) + r2 * (l1 + l2 * r2);
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// Compute final projected point position.
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const T& focal = camera[6];
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T predicted_x = focal * distortion * xp;
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T predicted_y = focal * distortion * yp;
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// The error is the difference between the predicted and observed position.
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residuals[0] = predicted_x - T(observed_x);
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residuals[1] = predicted_y - T(observed_y);
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return true;
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}
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// Factory to hide the construction of the CostFunction object from
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// the client code.
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static ceres::CostFunction* Create(const double observed_x,
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const double observed_y) {
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return (new ceres::AutoDiffCostFunction<SnavelyReprojectionError, 2, 9, 3>(
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new SnavelyReprojectionError(observed_x, observed_y)));
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}
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double observed_x;
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double observed_y;
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};
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int main(int argc, char** argv) {
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google::InitGoogleLogging(argv[0]);
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if (argc != 2) {
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std::cerr << "usage: simple_bundle_adjuster <bal_problem>\n";
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return 1;
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}
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BALProblem bal_problem;
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if (!bal_problem.LoadFile(argv[1])) {
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std::cerr << "ERROR: unable to open file " << argv[1] << "\n";
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return 1;
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}
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const double* observations = bal_problem.observations();
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// Create residuals for each observation in the bundle adjustment problem. The
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// parameters for cameras and points are added automatically.
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ceres::Problem problem;
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for (int i = 0; i < bal_problem.num_observations(); ++i) {
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// Each Residual block takes a point and a camera as input and outputs a 2
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// dimensional residual. Internally, the cost function stores the observed
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// image location and compares the reprojection against the observation.
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ceres::CostFunction* cost_function =
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SnavelyReprojectionError::Create(observations[2 * i + 0],
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observations[2 * i + 1]);
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problem.AddResidualBlock(cost_function,
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NULL /* squared loss */,
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bal_problem.mutable_camera_for_observation(i),
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bal_problem.mutable_point_for_observation(i));
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}
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// Make Ceres automatically detect the bundle structure. Note that the
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// standard solver, SPARSE_NORMAL_CHOLESKY, also works fine but it is slower
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// for standard bundle adjustment problems.
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ceres::Solver::Options options;
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options.linear_solver_type = ceres::DENSE_SCHUR;
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options.minimizer_progress_to_stdout = true;
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ceres::Solver::Summary summary;
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ceres::Solve(options, &problem, &summary);
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std::cout << summary.FullReport() << "\n";
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return 0;
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}
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