examples/bundle_adjuster.cc

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
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//
// 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_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_string(solver_log, "", "File to record the solver execution to.");
DEFINE_bool(line_search, false, "Use a line search instead of trust region "
            "algorithm.");

namespace ceres {
namespace examples {

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->num_linear_solver_threads = FLAGS_num_threads;
}

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);
    // If quaternions are used, there are two blocks, so add the
    // second block to the ordering.
    if (FLAGS_use_quaternions) {
      ordering->AddElementToGroup(cameras + camera_block_size * i + 4, 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.
    if (FLAGS_use_quaternions) {
      cost_function = new AutoDiffCostFunction<
          SnavelyReprojectionErrorWithQuaternions, 2, 4, 6, 3>(
              new SnavelyReprojectionErrorWithQuaternions(
                  observations[2 * i + 0],
                  observations[2 * i + 1]));
    } else {
      cost_function =
          new AutoDiffCostFunction<SnavelyReprojectionError, 2, 9, 3>(
              new SnavelyReprojectionError(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];

    if (FLAGS_use_quaternions) {
      // When using quaternions, we split the camera into two
      // parameter blocks. One of size 4 for the quaternion and the
      // other of size 6 containing the translation, focal length and
      // the radial distortion parameters.
      problem->AddResidualBlock(cost_function,
                                loss_function,
                                camera,
                                camera + 4,
                                point);
    } else {
      problem->AddResidualBlock(cost_function, loss_function, camera, point);
    }
  }

  if (FLAGS_use_quaternions && FLAGS_use_local_parameterization) {
    LocalParameterization* quaternion_parameterization =
         new QuaternionParameterization;
    for (int i = 0; i < bal_problem->num_cameras(); ++i) {
      problem->SetParameterization(cameras + camera_block_size * i,
                                   quaternion_parameterization);
    }
  }
}

void SolveProblem(const char* filename) {
  BALProblem bal_problem(filename, FLAGS_use_quaternions);
  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.solver_log = FLAGS_solver_log;
  options.gradient_tolerance = 1e-16;
  options.function_tolerance = 1e-16;
  Solver::Summary summary;
  Solve(options, &problem, &summary);
  std::cout << summary.FullReport() << "\n";
}

}  // namespace examples
}  // namespace ceres

int main(int argc, char** argv) {
  google::ParseCommandLineFlags(&argc, &argv, true);
  google::InitGoogleLogging(argv[0]);
  if (FLAGS_input.empty()) {
    LOG(ERROR) << "Usage: bundle_adjustment_example --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;
}