loransac_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 "optim/ransac"
#include "util/testing.h"
 
#include <Eigen/Core>
#include <Eigen/Geometry>
 
#include "base/pose.h"
#include "base/similarity_transform.h"
#include "estimators/similarity_transform.h"
#include "optim/loransac.h"
#include "util/random.h"
 
using namespace colmap;
 
BOOST_AUTO_TEST_CASE(TestReport) {
  LORANSAC<SimilarityTransformEstimator<3>,
           SimilarityTransformEstimator<3>>::Report report;
  BOOST_CHECK_EQUAL(report.success, false);
  BOOST_CHECK_EQUAL(report.num_trials, 0);
  BOOST_CHECK_EQUAL(report.support.num_inliers, 0);
  BOOST_CHECK_EQUAL(report.support.residual_sum,
                    std::numeric_limits<double>::max());
  BOOST_CHECK_EQUAL(report.inlier_mask.size(), 0);
}
 
BOOST_AUTO_TEST_CASE(TestSimilarityTransform) {
  SetPRNGSeed(0);
 
  const size_t num_samples = 1000;
  const size_t num_outliers = 400;
 
  // Create some arbitrary transformation.
  const SimilarityTransform3 orig_tform(2, ComposeIdentityQuaternion(),
                                        Eigen::Vector3d(100, 10, 10));
 
  // Generate exact data
  std::vector<Eigen::Vector3d> src;
  std::vector<Eigen::Vector3d> dst;
  for (size_t i = 0; i < num_samples; ++i) {
    src.emplace_back(i, std::sqrt(i) + 2, std::sqrt(2 * i + 2));
    dst.push_back(src.back());
    orig_tform.TransformPoint(&dst.back());
  }
 
  // Add some faulty data.
  for (size_t i = 0; i < num_outliers; ++i) {
    dst[i] = Eigen::Vector3d(RandomReal(-3000.0, -2000.0),
                             RandomReal(-4000.0, -3000.0),
                             RandomReal(-5000.0, -4000.0));
  }
 
  // Robustly estimate transformation using RANSAC.
  RANSACOptions options;
  options.max_error = 10;
  LORANSAC<SimilarityTransformEstimator<3>, SimilarityTransformEstimator<3>>
      ransac(options);
  const auto report = ransac.Estimate(src, dst);
 
  BOOST_CHECK_EQUAL(report.success, true);
  BOOST_CHECK_GT(report.num_trials, 0);
 
  // Make sure outliers were detected correctly.
  BOOST_CHECK_EQUAL(report.support.num_inliers, num_samples - num_outliers);
  for (size_t i = 0; i < num_samples; ++i) {
    if (i < num_outliers) {
      BOOST_CHECK(!report.inlier_mask[i]);
    } else {
      BOOST_CHECK(report.inlier_mask[i]);
    }
  }
 
  // Make sure original transformation is estimated correctly.
  const double matrix_diff =
      (orig_tform.Matrix().topLeftCorner<3, 4>() - report.model).norm();
  BOOST_CHECK(std::abs(matrix_diff) < 1e-6);
}