562 lines
19 KiB
C++
562 lines
19 KiB
C++
// 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.
|
|
// * 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)
|
|
// sameeragarwal@google.com (Sameer Agarwal)
|
|
//
|
|
// End-to-end bundle adjustment tests for Ceres. It uses a bundle
|
|
// adjustment problem with 16 cameras and two thousand points.
|
|
|
|
#include <cmath>
|
|
#include <cstdio>
|
|
#include <cstdlib>
|
|
#include <string>
|
|
|
|
#include "ceres/internal/port.h"
|
|
|
|
#include "ceres/autodiff_cost_function.h"
|
|
#include "ceres/ordered_groups.h"
|
|
#include "ceres/problem.h"
|
|
#include "ceres/rotation.h"
|
|
#include "ceres/solver.h"
|
|
#include "ceres/stringprintf.h"
|
|
#include "ceres/test_util.h"
|
|
#include "ceres/types.h"
|
|
#include "gflags/gflags.h"
|
|
#include "glog/logging.h"
|
|
#include "gtest/gtest.h"
|
|
|
|
namespace ceres {
|
|
namespace internal {
|
|
|
|
using std::string;
|
|
using std::vector;
|
|
|
|
const bool kAutomaticOrdering = true;
|
|
const bool kUserOrdering = false;
|
|
|
|
// This class implements the SystemTestProblem interface and provides
|
|
// access to a bundle adjustment problem. It is based on
|
|
// examples/bundle_adjustment_example.cc. Currently a small 16 camera
|
|
// problem is hard coded in the constructor.
|
|
class BundleAdjustmentProblem {
|
|
public:
|
|
BundleAdjustmentProblem() {
|
|
const string input_file = TestFileAbsolutePath("problem-16-22106-pre.txt");
|
|
ReadData(input_file);
|
|
BuildProblem();
|
|
}
|
|
|
|
~BundleAdjustmentProblem() {
|
|
delete []point_index_;
|
|
delete []camera_index_;
|
|
delete []observations_;
|
|
delete []parameters_;
|
|
}
|
|
|
|
Problem* mutable_problem() { return &problem_; }
|
|
Solver::Options* mutable_solver_options() { return &options_; }
|
|
|
|
int num_cameras() const { return num_cameras_; }
|
|
int num_points() const { return num_points_; }
|
|
int num_observations() const { return num_observations_; }
|
|
const int* point_index() const { return point_index_; }
|
|
const int* camera_index() const { return camera_index_; }
|
|
const double* observations() const { return observations_; }
|
|
double* mutable_cameras() { return parameters_; }
|
|
double* mutable_points() { return parameters_ + 9 * num_cameras_; }
|
|
|
|
static double kResidualTolerance;
|
|
|
|
private:
|
|
void ReadData(const string& filename) {
|
|
FILE * fptr = fopen(filename.c_str(), "r");
|
|
|
|
if (!fptr) {
|
|
LOG(FATAL) << "File Error: unable to open file " << filename;
|
|
}
|
|
|
|
// This will die horribly on invalid files. Them's the breaks.
|
|
FscanfOrDie(fptr, "%d", &num_cameras_);
|
|
FscanfOrDie(fptr, "%d", &num_points_);
|
|
FscanfOrDie(fptr, "%d", &num_observations_);
|
|
|
|
VLOG(1) << "Header: " << num_cameras_
|
|
<< " " << num_points_
|
|
<< " " << 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);
|
|
}
|
|
}
|
|
|
|
void BuildProblem() {
|
|
double* points = mutable_points();
|
|
double* cameras = mutable_cameras();
|
|
|
|
for (int i = 0; i < num_observations(); ++i) {
|
|
// Each Residual block takes a point and a camera as input and
|
|
// outputs a 2 dimensional residual.
|
|
CostFunction* cost_function =
|
|
new AutoDiffCostFunction<BundlerResidual, 2, 9, 3>(
|
|
new BundlerResidual(observations_[2*i + 0],
|
|
observations_[2*i + 1]));
|
|
|
|
// 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 + 9 * camera_index_[i];
|
|
double* point = points + 3 * point_index()[i];
|
|
problem_.AddResidualBlock(cost_function, NULL, camera, point);
|
|
}
|
|
|
|
options_.linear_solver_ordering.reset(new ParameterBlockOrdering);
|
|
|
|
// The points come before the cameras.
|
|
for (int i = 0; i < num_points_; ++i) {
|
|
options_.linear_solver_ordering->AddElementToGroup(points + 3 * i, 0);
|
|
}
|
|
|
|
for (int i = 0; i < num_cameras_; ++i) {
|
|
options_.linear_solver_ordering->AddElementToGroup(cameras + 9 * i, 1);
|
|
}
|
|
|
|
options_.linear_solver_type = DENSE_SCHUR;
|
|
options_.max_num_iterations = 25;
|
|
options_.function_tolerance = 1e-10;
|
|
options_.gradient_tolerance = 1e-10;
|
|
options_.parameter_tolerance = 1e-10;
|
|
}
|
|
|
|
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.";
|
|
}
|
|
}
|
|
|
|
// Templated pinhole camera model. 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 to be located at the image
|
|
// center).
|
|
struct BundlerResidual {
|
|
// (u, v): the position of the observation with respect to the image
|
|
// center point.
|
|
BundlerResidual(double u, double v): u(u), v(v) {}
|
|
|
|
template <typename T>
|
|
bool operator()(const T* const camera,
|
|
const T* const point,
|
|
T* residuals) const {
|
|
T p[3];
|
|
AngleAxisRotatePoint(camera, point, p);
|
|
|
|
// Add the translation vector
|
|
p[0] += camera[3];
|
|
p[1] += camera[4];
|
|
p[2] += camera[5];
|
|
|
|
const T& focal = camera[6];
|
|
const T& l1 = camera[7];
|
|
const T& l2 = camera[8];
|
|
|
|
// 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 = - focal * p[0] / p[2];
|
|
T yp = - focal * p[1] / p[2];
|
|
|
|
// Apply second and fourth order radial distortion.
|
|
T r2 = xp*xp + yp*yp;
|
|
T distortion = T(1.0) + r2 * (l1 + l2 * r2);
|
|
|
|
residuals[0] = distortion * xp - T(u);
|
|
residuals[1] = distortion * yp - T(v);
|
|
|
|
return true;
|
|
}
|
|
|
|
double u;
|
|
double v;
|
|
};
|
|
|
|
Problem problem_;
|
|
Solver::Options options_;
|
|
|
|
int num_cameras_;
|
|
int num_points_;
|
|
int num_observations_;
|
|
int num_parameters_;
|
|
|
|
int* point_index_;
|
|
int* camera_index_;
|
|
double* observations_;
|
|
// The parameter vector is laid out as follows
|
|
// [camera_1, ..., camera_n, point_1, ..., point_m]
|
|
double* parameters_;
|
|
};
|
|
|
|
double BundleAdjustmentProblem::kResidualTolerance = 1e-4;
|
|
typedef SystemTest<BundleAdjustmentProblem> BundleAdjustmentTest;
|
|
|
|
TEST_F(BundleAdjustmentTest, DenseSchurWithAutomaticOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(DENSE_SCHUR, NO_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, DenseSchurWithUserOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(DENSE_SCHUR, NO_SPARSE, kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, IterativeSchurWithJacobiAndAutomaticOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(ITERATIVE_SCHUR, NO_SPARSE, kAutomaticOrdering, JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, IterativeSchurWithJacobiAndUserOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(ITERATIVE_SCHUR, NO_SPARSE, kUserOrdering, JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
IterativeSchurWithSchurJacobiAndAutomaticOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(ITERATIVE_SCHUR,
|
|
NO_SPARSE,
|
|
kAutomaticOrdering,
|
|
SCHUR_JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, IterativeSchurWithSchurJacobiAndUserOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(ITERATIVE_SCHUR, NO_SPARSE, kUserOrdering, SCHUR_JACOBI));
|
|
}
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
TEST_F(BundleAdjustmentTest,
|
|
SparseNormalCholeskyWithAutomaticOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
SparseNormalCholeskyWithUserOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE, kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
SparseSchurWithAutomaticOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_SCHUR, SUITE_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, SparseSchurWithUserOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_SCHUR, SUITE_SPARSE, kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
IterativeSchurWithClusterJacobiAndAutomaticOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(ITERATIVE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kAutomaticOrdering,
|
|
CLUSTER_JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
IterativeSchurWithClusterJacobiAndUserOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(ITERATIVE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kUserOrdering,
|
|
CLUSTER_JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
IterativeSchurWithClusterTridiagonalAndAutomaticOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(ITERATIVE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kAutomaticOrdering,
|
|
CLUSTER_TRIDIAGONAL));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
IterativeSchurWithClusterTridiagonalAndUserOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(ITERATIVE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kUserOrdering,
|
|
CLUSTER_TRIDIAGONAL));
|
|
}
|
|
#endif // CERES_NO_SUITESPARSE
|
|
|
|
#ifndef CERES_NO_CXSPARSE
|
|
TEST_F(BundleAdjustmentTest,
|
|
SparseNormalCholeskyWithAutomaticOrderingUsingCXSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_NORMAL_CHOLESKY, CX_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
SparseNormalCholeskyWithUserOrderingUsingCXSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_NORMAL_CHOLESKY, CX_SPARSE, kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, SparseSchurWithAutomaticOrderingUsingCXSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_SCHUR, CX_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, SparseSchurWithUserOrderingUsingCXSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_SCHUR, CX_SPARSE, kUserOrdering));
|
|
}
|
|
#endif // CERES_NO_CXSPARSE
|
|
|
|
#ifdef CERES_USE_EIGEN_SPARSE
|
|
TEST_F(BundleAdjustmentTest,
|
|
SparseNormalCholeskyWithAutomaticOrderingUsingEigenSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_NORMAL_CHOLESKY, EIGEN_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
SparseNormalCholeskyWithUserOrderingUsingEigenSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_NORMAL_CHOLESKY, EIGEN_SPARSE, kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
SparseSchurWithAutomaticOrderingUsingEigenSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_SCHUR, EIGEN_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, SparseSchurWithUserOrderingUsingEigenSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
SolverConfig(SPARSE_SCHUR, EIGEN_SPARSE, kUserOrdering));
|
|
}
|
|
#endif // CERES_USE_EIGEN_SPARSE
|
|
|
|
#ifdef CERES_USE_OPENMP
|
|
|
|
TEST_F(BundleAdjustmentTest, MultiThreadedDenseSchurWithAutomaticOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(DENSE_SCHUR, NO_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest, MultiThreadedDenseSchurWithUserOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(DENSE_SCHUR, NO_SPARSE, kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedIterativeSchurWithJacobiAndAutomaticOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(ITERATIVE_SCHUR,
|
|
NO_SPARSE,
|
|
kAutomaticOrdering,
|
|
JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedIterativeSchurWithJacobiAndUserOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(ITERATIVE_SCHUR, NO_SPARSE, kUserOrdering, JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedIterativeSchurWithSchurJacobiAndAutomaticOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(ITERATIVE_SCHUR,
|
|
NO_SPARSE,
|
|
kAutomaticOrdering,
|
|
SCHUR_JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedIterativeSchurWithSchurJacobiAndUserOrdering) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(ITERATIVE_SCHUR,
|
|
NO_SPARSE,
|
|
kUserOrdering,
|
|
SCHUR_JACOBI));
|
|
}
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseNormalCholeskyWithAutomaticOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
|
|
SUITE_SPARSE,
|
|
kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseNormalCholeskyWithUserOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
|
|
SUITE_SPARSE,
|
|
kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseSchurWithAutomaticOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseSchurWithUserOrderingUsingSuiteSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_SCHUR, SUITE_SPARSE, kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedIterativeSchurWithClusterJacobiAndAutomaticOrderingUsingSuiteSparse) { // NOLINT
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(ITERATIVE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kAutomaticOrdering,
|
|
CLUSTER_JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedIterativeSchurWithClusterJacobiAndUserOrderingUsingSuiteSparse) { // NOLINT
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(ITERATIVE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kUserOrdering,
|
|
CLUSTER_JACOBI));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedIterativeSchurWithClusterTridiagonalAndAutomaticOrderingUsingSuiteSparse) { // NOLINT
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(ITERATIVE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kAutomaticOrdering,
|
|
CLUSTER_TRIDIAGONAL));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedIterativeSchurWithClusterTridiagonalAndUserOrderingUsingSuiteSparse) { // NOTLINT
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(ITERATIVE_SCHUR,
|
|
SUITE_SPARSE,
|
|
kUserOrdering,
|
|
CLUSTER_TRIDIAGONAL));
|
|
}
|
|
#endif // CERES_NO_SUITESPARSE
|
|
|
|
#ifndef CERES_NO_CXSPARSE
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseNormalCholeskyWithAutomaticOrderingUsingCXSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
|
|
CX_SPARSE,
|
|
kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseNormalCholeskyWithUserOrderingUsingCXSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY, CX_SPARSE, kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseSchurWithAutomaticOrderingUsingCXSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_SCHUR, CX_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseSchurWithUserOrderingUsingCXSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_SCHUR, CX_SPARSE, kUserOrdering));
|
|
}
|
|
#endif // CERES_NO_CXSPARSE
|
|
|
|
#ifdef CERES_USE_EIGEN_SPARSE
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseNormalCholeskyWithAutomaticOrderingUsingEigenSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
|
|
EIGEN_SPARSE,
|
|
kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseNormalCholeskyWithUserOrderingUsingEigenSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
|
|
EIGEN_SPARSE,
|
|
kUserOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseSchurWithAutomaticOrderingUsingEigenSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_SCHUR, EIGEN_SPARSE, kAutomaticOrdering));
|
|
}
|
|
|
|
TEST_F(BundleAdjustmentTest,
|
|
MultiThreadedSparseSchurWithUserOrderingUsingEigenSparse) {
|
|
RunSolverForConfigAndExpectResidualsMatch(
|
|
ThreadedSolverConfig(SPARSE_SCHUR, EIGEN_SPARSE, kUserOrdering));
|
|
}
|
|
#endif // CERES_USE_EIGEN_SPARSE
|
|
#endif // CERES_USE_OPENMP
|
|
|
|
} // namespace internal
|
|
} // namespace ceres
|