122 lines
4.5 KiB
C++
122 lines
4.5 KiB
C++
// An example showing TEASER++ registration with the Stanford bunny model
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#include <chrono>
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#include <iostream>
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#include <random>
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#include <Eigen/Core>
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#include <teaser/ply_io.h>
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#include <teaser/registration.h>
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// Macro constants for generating noise and outliers
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#define NOISE_BOUND 0.05
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#define N_OUTLIERS 1700
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#define OUTLIER_TRANSLATION_LB 5
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#define OUTLIER_TRANSLATION_UB 10
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inline double getAngularError(Eigen::Matrix3d R_exp, Eigen::Matrix3d R_est) {
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return std::abs(std::acos(fmin(fmax(((R_exp.transpose() * R_est).trace() - 1) / 2, -1.0), 1.0)));
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}
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void addNoiseAndOutliers(Eigen::Matrix<double, 3, Eigen::Dynamic>& tgt) {
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// Add uniform noise
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Eigen::Matrix<double, 3, Eigen::Dynamic> noise =
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Eigen::Matrix<double, 3, Eigen::Dynamic>::Random(3, tgt.cols()) * NOISE_BOUND;
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NOISE_BOUND / 2;
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tgt = tgt + noise;
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// Add outliers
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std::random_device rd;
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std::mt19937 gen(rd());
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std::uniform_int_distribution<> dis2(0, tgt.cols() - 1); // pos of outliers
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std::uniform_int_distribution<> dis3(OUTLIER_TRANSLATION_LB,
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OUTLIER_TRANSLATION_UB); // random translation
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std::vector<bool> expected_outlier_mask(tgt.cols(), false);
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for (int i = 0; i < N_OUTLIERS; ++i) {
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int c_outlier_idx = dis2(gen);
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assert(c_outlier_idx < expected_outlier_mask.size());
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expected_outlier_mask[c_outlier_idx] = true;
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tgt.col(c_outlier_idx).array() += dis3(gen); // random translation
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}
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}
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int main() {
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// Load the .ply file
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teaser::PLYReader reader;
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teaser::PointCloud src_cloud;
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auto status = reader.read("./example_data/bun_zipper_res3.ply", src_cloud);
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int N = src_cloud.size();
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// Convert the point cloud to Eigen
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Eigen::Matrix<double, 3, Eigen::Dynamic> src(3, N);
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for (size_t i = 0; i < N; ++i) {
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src.col(i) << src_cloud[i].x, src_cloud[i].y, src_cloud[i].z;
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}
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// Homogeneous coordinates
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Eigen::Matrix<double, 4, Eigen::Dynamic> src_h;
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src_h.resize(4, src.cols());
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src_h.topRows(3) = src;
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src_h.bottomRows(1) = Eigen::Matrix<double, 1, Eigen::Dynamic>::Ones(N);
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// Apply an arbitrary SE(3) transformation
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Eigen::Matrix4d T;
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// clang-format off
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T << 9.96926560e-01, 6.68735757e-02, -4.06664421e-02, -1.15576939e-01,
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-6.61289946e-02, 9.97617877e-01, 1.94008687e-02, -3.87705398e-02,
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4.18675510e-02, -1.66517807e-02, 9.98977765e-01, 1.14874890e-01,
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0, 0, 0, 1;
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// clang-format on
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// Apply transformation
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Eigen::Matrix<double, 4, Eigen::Dynamic> tgt_h = T * src_h;
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Eigen::Matrix<double, 3, Eigen::Dynamic> tgt = tgt_h.topRows(3);
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// Add some noise & outliers
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addNoiseAndOutliers(tgt);
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// Run TEASER++ registration
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// Prepare solver parameters
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teaser::RobustRegistrationSolver::Params params;
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params.noise_bound = NOISE_BOUND;
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params.cbar2 = 1;
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params.estimate_scaling = false;
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params.rotation_max_iterations = 100;
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params.rotation_gnc_factor = 1.4;
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params.rotation_estimation_algorithm =
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teaser::RobustRegistrationSolver::ROTATION_ESTIMATION_ALGORITHM::GNC_TLS;
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params.rotation_cost_threshold = 0.005;
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// Solve with TEASER++
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teaser::RobustRegistrationSolver solver(params);
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std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
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solver.solve(src, tgt);
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std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
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auto solution = solver.getSolution();
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// Compare results
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std::cout << "=====================================" << std::endl;
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std::cout << " TEASER++ Results " << std::endl;
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std::cout << "=====================================" << std::endl;
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std::cout << "Expected rotation: " << std::endl;
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std::cout << T.topLeftCorner(3, 3) << std::endl;
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std::cout << "Estimated rotation: " << std::endl;
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std::cout << solution.rotation << std::endl;
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std::cout << "Error (deg): " << getAngularError(T.topLeftCorner(3, 3), solution.rotation)
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<< std::endl;
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std::cout << std::endl;
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std::cout << "Expected translation: " << std::endl;
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std::cout << T.topRightCorner(3, 1) << std::endl;
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std::cout << "Estimated translation: " << std::endl;
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std::cout << solution.translation << std::endl;
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std::cout << "Error (m): " << (T.topRightCorner(3, 1) - solution.translation).norm() << std::endl;
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std::cout << std::endl;
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std::cout << "Number of correspondences: " << N << std::endl;
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std::cout << "Number of outliers: " << N_OUTLIERS << std::endl;
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std::cout << "Time taken (s): "
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<< std::chrono::duration_cast<std::chrono::microseconds>(end - begin).count() /
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1000000.0
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<< std::endl;
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}
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