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