// Ceres Solver - A fast non-linear least squares minimizer // Copyright 2023 Google Inc. All rights reserved. // http://ceres-solver.org/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // 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 #include #include #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 == nullptr) { 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 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 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. T xp = -p[0] / p[2]; T yp = -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 = 1.0 + r2 * (l1 + l2 * r2); // Compute final projected point position. const T& focal = camera[6]; T predicted_x = focal * distortion * xp; T predicted_y = focal * distortion * yp; // The error is the difference between the predicted and observed position. residuals[0] = predicted_x - observed_x; residuals[1] = predicted_y - observed_y; return true; } // Factory to hide the construction of the CostFunction object from // the client code. static ceres::CostFunction* Create(const double observed_x, const double observed_y) { return (new ceres::AutoDiffCostFunction( new SnavelyReprojectionError(observed_x, observed_y))); } 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 \n"; return 1; } BALProblem bal_problem; if (!bal_problem.LoadFile(argv[1])) { std::cerr << "ERROR: unable to open file " << argv[1] << "\n"; return 1; } const double* observations = bal_problem.observations(); // 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 = SnavelyReprojectionError::Create( observations[2 * i + 0], observations[2 * i + 1]); problem.AddResidualBlock(cost_function, nullptr /* 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.minimizer_progress_to_stdout = true; ceres::Solver::Summary summary; ceres::Solve(options, &problem, &summary); std::cout << summary.FullReport() << "\n"; return 0; }