| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2015 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. |
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| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
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| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| // |
| // An example of solving a dynamically sized problem with various |
| // solvers and loss functions. |
| // |
| // For a simpler bare bones example of doing bundle adjustment with |
| // Ceres, please see simple_bundle_adjuster.cc. |
| // |
| // NOTE: This example will not compile without gflags and SuiteSparse. |
| // |
| // The problem being solved here is known as a Bundle Adjustment |
| // problem in computer vision. Given a set of 3d points X_1, ..., X_n, |
| // a set of cameras P_1, ..., P_m. If the point X_i is visible in |
| // image j, then there is a 2D observation u_ij that is the expected |
| // projection of X_i using P_j. The aim of this optimization is to |
| // find values of X_i and P_j such that the reprojection error |
| // |
| // E(X,P) = sum_ij |u_ij - P_j X_i|^2 |
| // |
| // is minimized. |
| // |
| // The problem used here comes from a collection of bundle adjustment |
| // problems published at University of Washington. |
| // http://grail.cs.washington.edu/projects/bal |
| |
| #include <algorithm> |
| #include <cmath> |
| #include <cstdio> |
| #include <cstdlib> |
| #include <string> |
| #include <vector> |
| |
| #include "bal_problem.h" |
| #include "ceres/ceres.h" |
| #include "gflags/gflags.h" |
| #include "glog/logging.h" |
| #include "snavely_reprojection_error.h" |
| |
| DEFINE_string(input, "", "Input File name"); |
| DEFINE_string(trust_region_strategy, "levenberg_marquardt", |
| "Options are: levenberg_marquardt, dogleg."); |
| DEFINE_string(dogleg, "traditional_dogleg", "Options are: traditional_dogleg," |
| "subspace_dogleg."); |
| |
| DEFINE_bool(inner_iterations, false, "Use inner iterations to non-linearly " |
| "refine each successful trust region step."); |
| |
| DEFINE_string(blocks_for_inner_iterations, "automatic", "Options are: " |
| "automatic, cameras, points, cameras,points, points,cameras"); |
| |
| DEFINE_string(linear_solver, "sparse_schur", "Options are: " |
| "sparse_schur, dense_schur, iterative_schur, sparse_normal_cholesky, " |
| "dense_qr, dense_normal_cholesky and cgnr."); |
| DEFINE_bool(explicit_schur_complement, false, "If using ITERATIVE_SCHUR " |
| "then explicitly compute the Schur complement."); |
| DEFINE_string(preconditioner, "jacobi", "Options are: " |
| "identity, jacobi, schur_jacobi, cluster_jacobi, " |
| "cluster_tridiagonal."); |
| DEFINE_string(visibility_clustering, "canonical_views", |
| "single_linkage, canonical_views"); |
| |
| DEFINE_string(sparse_linear_algebra_library, "suite_sparse", |
| "Options are: suite_sparse and cx_sparse."); |
| DEFINE_string(dense_linear_algebra_library, "eigen", |
| "Options are: eigen and lapack."); |
| DEFINE_string(ordering, "automatic", "Options are: automatic, user."); |
| |
| DEFINE_bool(use_quaternions, false, "If true, uses quaternions to represent " |
| "rotations. If false, angle axis is used."); |
| DEFINE_bool(use_local_parameterization, false, "For quaternions, use a local " |
| "parameterization."); |
| DEFINE_bool(robustify, false, "Use a robust loss function."); |
| |
| DEFINE_double(eta, 1e-2, "Default value for eta. Eta determines the " |
| "accuracy of each linear solve of the truncated newton step. " |
| "Changing this parameter can affect solve performance."); |
| |
| DEFINE_int32(num_threads, 1, "Number of threads."); |
| DEFINE_int32(num_iterations, 5, "Number of iterations."); |
| DEFINE_double(max_solver_time, 1e32, "Maximum solve time in seconds."); |
| DEFINE_bool(nonmonotonic_steps, false, "Trust region algorithm can use" |
| " nonmonotic steps."); |
| |
| DEFINE_double(rotation_sigma, 0.0, "Standard deviation of camera rotation " |
| "perturbation."); |
| DEFINE_double(translation_sigma, 0.0, "Standard deviation of the camera " |
| "translation perturbation."); |
| DEFINE_double(point_sigma, 0.0, "Standard deviation of the point " |
| "perturbation."); |
| DEFINE_int32(random_seed, 38401, "Random seed used to set the state " |
| "of the pseudo random number generator used to generate " |
| "the pertubations."); |
| DEFINE_bool(line_search, false, "Use a line search instead of trust region " |
| "algorithm."); |
| DEFINE_bool(mixed_precision_solves, false, "Use mixed precision solves."); |
| DEFINE_int32(max_num_refinement_iterations, 0, "Iterative refinement iterations"); |
| DEFINE_string(initial_ply, "", "Export the BAL file data as a PLY file."); |
| DEFINE_string(final_ply, "", "Export the refined BAL file data as a PLY " |
| "file."); |
| |
| namespace ceres { |
| namespace examples { |
| namespace { |
| |
| void SetLinearSolver(Solver::Options* options) { |
| CHECK(StringToLinearSolverType(FLAGS_linear_solver, |
| &options->linear_solver_type)); |
| CHECK(StringToPreconditionerType(FLAGS_preconditioner, |
| &options->preconditioner_type)); |
| CHECK(StringToVisibilityClusteringType(FLAGS_visibility_clustering, |
| &options->visibility_clustering_type)); |
| CHECK(StringToSparseLinearAlgebraLibraryType( |
| FLAGS_sparse_linear_algebra_library, |
| &options->sparse_linear_algebra_library_type)); |
| CHECK(StringToDenseLinearAlgebraLibraryType( |
| FLAGS_dense_linear_algebra_library, |
| &options->dense_linear_algebra_library_type)); |
| options->use_explicit_schur_complement = FLAGS_explicit_schur_complement; |
| options->use_mixed_precision_solves = FLAGS_mixed_precision_solves; |
| options->max_num_refinement_iterations = FLAGS_max_num_refinement_iterations; |
| } |
| |
| void SetOrdering(BALProblem* bal_problem, Solver::Options* options) { |
| const int num_points = bal_problem->num_points(); |
| const int point_block_size = bal_problem->point_block_size(); |
| double* points = bal_problem->mutable_points(); |
| |
| const int num_cameras = bal_problem->num_cameras(); |
| const int camera_block_size = bal_problem->camera_block_size(); |
| double* cameras = bal_problem->mutable_cameras(); |
| |
| if (options->use_inner_iterations) { |
| if (FLAGS_blocks_for_inner_iterations == "cameras") { |
| LOG(INFO) << "Camera blocks for inner iterations"; |
| options->inner_iteration_ordering.reset(new ParameterBlockOrdering); |
| for (int i = 0; i < num_cameras; ++i) { |
| options->inner_iteration_ordering->AddElementToGroup(cameras + camera_block_size * i, 0); |
| } |
| } else if (FLAGS_blocks_for_inner_iterations == "points") { |
| LOG(INFO) << "Point blocks for inner iterations"; |
| options->inner_iteration_ordering.reset(new ParameterBlockOrdering); |
| for (int i = 0; i < num_points; ++i) { |
| options->inner_iteration_ordering->AddElementToGroup(points + point_block_size * i, 0); |
| } |
| } else if (FLAGS_blocks_for_inner_iterations == "cameras,points") { |
| LOG(INFO) << "Camera followed by point blocks for inner iterations"; |
| options->inner_iteration_ordering.reset(new ParameterBlockOrdering); |
| for (int i = 0; i < num_cameras; ++i) { |
| options->inner_iteration_ordering->AddElementToGroup(cameras + camera_block_size * i, 0); |
| } |
| for (int i = 0; i < num_points; ++i) { |
| options->inner_iteration_ordering->AddElementToGroup(points + point_block_size * i, 1); |
| } |
| } else if (FLAGS_blocks_for_inner_iterations == "points,cameras") { |
| LOG(INFO) << "Point followed by camera blocks for inner iterations"; |
| options->inner_iteration_ordering.reset(new ParameterBlockOrdering); |
| for (int i = 0; i < num_cameras; ++i) { |
| options->inner_iteration_ordering->AddElementToGroup(cameras + camera_block_size * i, 1); |
| } |
| for (int i = 0; i < num_points; ++i) { |
| options->inner_iteration_ordering->AddElementToGroup(points + point_block_size * i, 0); |
| } |
| } else if (FLAGS_blocks_for_inner_iterations == "automatic") { |
| LOG(INFO) << "Choosing automatic blocks for inner iterations"; |
| } else { |
| LOG(FATAL) << "Unknown block type for inner iterations: " |
| << FLAGS_blocks_for_inner_iterations; |
| } |
| } |
| |
| // Bundle adjustment problems have a sparsity structure that makes |
| // them amenable to more specialized and much more efficient |
| // solution strategies. The SPARSE_SCHUR, DENSE_SCHUR and |
| // ITERATIVE_SCHUR solvers make use of this specialized |
| // structure. |
| // |
| // This can either be done by specifying Options::ordering_type = |
| // ceres::SCHUR, in which case Ceres will automatically determine |
| // the right ParameterBlock ordering, or by manually specifying a |
| // suitable ordering vector and defining |
| // Options::num_eliminate_blocks. |
| if (FLAGS_ordering == "automatic") { |
| return; |
| } |
| |
| ceres::ParameterBlockOrdering* ordering = |
| new ceres::ParameterBlockOrdering; |
| |
| // The points come before the cameras. |
| for (int i = 0; i < num_points; ++i) { |
| ordering->AddElementToGroup(points + point_block_size * i, 0); |
| } |
| |
| for (int i = 0; i < num_cameras; ++i) { |
| // When using axis-angle, there is a single parameter block for |
| // the entire camera. |
| ordering->AddElementToGroup(cameras + camera_block_size * i, 1); |
| } |
| |
| options->linear_solver_ordering.reset(ordering); |
| } |
| |
| void SetMinimizerOptions(Solver::Options* options) { |
| options->max_num_iterations = FLAGS_num_iterations; |
| options->minimizer_progress_to_stdout = true; |
| options->num_threads = FLAGS_num_threads; |
| options->eta = FLAGS_eta; |
| options->max_solver_time_in_seconds = FLAGS_max_solver_time; |
| options->use_nonmonotonic_steps = FLAGS_nonmonotonic_steps; |
| if (FLAGS_line_search) { |
| options->minimizer_type = ceres::LINE_SEARCH; |
| } |
| |
| CHECK(StringToTrustRegionStrategyType(FLAGS_trust_region_strategy, |
| &options->trust_region_strategy_type)); |
| CHECK(StringToDoglegType(FLAGS_dogleg, &options->dogleg_type)); |
| options->use_inner_iterations = FLAGS_inner_iterations; |
| } |
| |
| void SetSolverOptionsFromFlags(BALProblem* bal_problem, |
| Solver::Options* options) { |
| SetMinimizerOptions(options); |
| SetLinearSolver(options); |
| SetOrdering(bal_problem, options); |
| } |
| |
| void BuildProblem(BALProblem* bal_problem, Problem* problem) { |
| const int point_block_size = bal_problem->point_block_size(); |
| const int camera_block_size = bal_problem->camera_block_size(); |
| double* points = bal_problem->mutable_points(); |
| double* cameras = bal_problem->mutable_cameras(); |
| |
| // Observations is 2*num_observations long array observations = |
| // [u_1, u_2, ... , u_n], where each u_i is two dimensional, the x |
| // and y positions of the observation. |
| const double* observations = bal_problem->observations(); |
| for (int i = 0; i < bal_problem->num_observations(); ++i) { |
| CostFunction* cost_function; |
| // Each Residual block takes a point and a camera as input and |
| // outputs a 2 dimensional residual. |
| cost_function = |
| (FLAGS_use_quaternions) |
| ? SnavelyReprojectionErrorWithQuaternions::Create( |
| observations[2 * i + 0], |
| observations[2 * i + 1]) |
| : SnavelyReprojectionError::Create( |
| observations[2 * i + 0], |
| observations[2 * i + 1]); |
| |
| // If enabled use Huber's loss function. |
| LossFunction* loss_function = FLAGS_robustify ? new HuberLoss(1.0) : NULL; |
| |
| // Each observation correponds to a pair of a camera and a point |
| // which are identified by camera_index()[i] and point_index()[i] |
| // respectively. |
| double* camera = |
| cameras + camera_block_size * bal_problem->camera_index()[i]; |
| double* point = points + point_block_size * bal_problem->point_index()[i]; |
| problem->AddResidualBlock(cost_function, loss_function, camera, point); |
| } |
| |
| if (FLAGS_use_quaternions && FLAGS_use_local_parameterization) { |
| LocalParameterization* camera_parameterization = |
| new ProductParameterization( |
| new QuaternionParameterization(), |
| new IdentityParameterization(6)); |
| for (int i = 0; i < bal_problem->num_cameras(); ++i) { |
| problem->SetParameterization(cameras + camera_block_size * i, |
| camera_parameterization); |
| } |
| } |
| } |
| |
| void SolveProblem(const char* filename) { |
| BALProblem bal_problem(filename, FLAGS_use_quaternions); |
| |
| if (!FLAGS_initial_ply.empty()) { |
| bal_problem.WriteToPLYFile(FLAGS_initial_ply); |
| } |
| |
| Problem problem; |
| |
| srand(FLAGS_random_seed); |
| bal_problem.Normalize(); |
| bal_problem.Perturb(FLAGS_rotation_sigma, |
| FLAGS_translation_sigma, |
| FLAGS_point_sigma); |
| |
| BuildProblem(&bal_problem, &problem); |
| Solver::Options options; |
| SetSolverOptionsFromFlags(&bal_problem, &options); |
| options.gradient_tolerance = 1e-16; |
| options.function_tolerance = 1e-16; |
| Solver::Summary summary; |
| Solve(options, &problem, &summary); |
| std::cout << summary.FullReport() << "\n"; |
| |
| if (!FLAGS_final_ply.empty()) { |
| bal_problem.WriteToPLYFile(FLAGS_final_ply); |
| } |
| } |
| |
| } // namespace |
| } // namespace examples |
| } // namespace ceres |
| |
| int main(int argc, char** argv) { |
| GFLAGS_NAMESPACE::ParseCommandLineFlags(&argc, &argv, true); |
| google::InitGoogleLogging(argv[0]); |
| if (FLAGS_input.empty()) { |
| LOG(ERROR) << "Usage: bundle_adjuster --input=bal_problem"; |
| return 1; |
| } |
| |
| CHECK(FLAGS_use_quaternions || !FLAGS_use_local_parameterization) |
| << "--use_local_parameterization can only be used with " |
| << "--use_quaternions."; |
| ceres::examples::SolveProblem(FLAGS_input.c_str()); |
| return 0; |
| } |