generalized_relative_pose_test.cc

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// Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
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// Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
 
#define TEST_NAME "base/generalized_relative_pose"
#include "util/testing.h"
 
#include <array>
 
#include "base/pose.h"
#include "base/projection.h"
#include "base/similarity_transform.h"
#include "estimators/generalized_relative_pose.h"
#include "optim/loransac.h"
#include "util/random.h"
 
using namespace colmap;
 
BOOST_AUTO_TEST_CASE(Estimate) {
  SetPRNGSeed(0);
 
  const size_t kNumPoints = 100;
 
  std::vector<Eigen::Vector3d> points3D;
  for (size_t i = 0; i < kNumPoints; ++i) {
    points3D.emplace_back(RandomReal<double>(-10, 10),
                          RandomReal<double>(-10, 10),
                          RandomReal<double>(-10, 10));
  }
 
  for (double qx = 0; qx < 0.4; qx += 0.1) {
    for (double tx = 0; tx < 0.5; tx += 0.1) {
      const int kRefTform = 1;
      const int kNumTforms = 3;
 
      const std::array<SimilarityTransform3, kNumTforms> orig_tforms = {{
          SimilarityTransform3(1, Eigen::Vector4d(1, qx, 0, 0),
                               Eigen::Vector3d(tx, 0.1, 0)),
          SimilarityTransform3(1, Eigen::Vector4d(1, qx + 0.05, 0, 0),
                               Eigen::Vector3d(tx, 0.2, 0)),
          SimilarityTransform3(1, Eigen::Vector4d(1, qx + 0.1, 0, 0),
                               Eigen::Vector3d(tx, 0.3, 0)),
      }};
 
      std::array<Eigen::Matrix3x4d, kNumTforms> rel_tforms;
      for (size_t i = 0; i < kNumTforms; ++i) {
        Eigen::Vector4d rel_qvec;
        Eigen::Vector3d rel_tvec;
        ComputeRelativePose(orig_tforms[kRefTform].Rotation(),
                            orig_tforms[kRefTform].Translation(),
                            orig_tforms[i].Rotation(),
                            orig_tforms[i].Translation(), &rel_qvec, &rel_tvec);
        rel_tforms[i] = ComposeProjectionMatrix(rel_qvec, rel_tvec);
      }
 
      // Project points to cameras.
      std::vector<GR6PEstimator::X_t> points1;
      std::vector<GR6PEstimator::Y_t> points2;
      for (size_t i = 0; i < points3D.size(); ++i) {
        const Eigen::Vector3d point3D_camera1 =
            rel_tforms[i % kNumTforms] * points3D[i].homogeneous();
        Eigen::Vector3d point3D_camera2 = points3D[i];
        orig_tforms[(i + 1) % kNumTforms].TransformPoint(&point3D_camera2);
 
        if (point3D_camera1.z() < 0 || point3D_camera2.z() < 0) {
          continue;
        }
 
        points1.emplace_back();
        points1.back().rel_tform = rel_tforms[i % kNumTforms];
        points1.back().xy = point3D_camera1.hnormalized();
 
        points2.emplace_back();
        points2.back().rel_tform = rel_tforms[(i + 1) % kNumTforms];
        points2.back().xy = point3D_camera2.hnormalized();
      }
 
      RANSACOptions options;
      options.max_error = 1e-3;
      LORANSAC<GR6PEstimator, GR6PEstimator> ransac(options);
      const auto report = ransac.Estimate(points1, points2);
 
      BOOST_CHECK_EQUAL(report.success, true);
 
      const double matrix_diff =
          (orig_tforms[kRefTform].Matrix().topLeftCorner<3, 4>() - report.model)
              .norm();
      BOOST_CHECK_LE(matrix_diff, 1e-2);
 
      std::vector<double> residuals;
      GR6PEstimator::Residuals(points1, points2, report.model, &residuals);
      for (size_t i = 0; i < residuals.size(); ++i) {
        BOOST_CHECK_LE(residuals[i], options.max_error);
      }
    }
  }
}