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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// The MIT License (MIT)
//
// Copyright (c) 2018-2021 www.open3d.org
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
// IN THE SOFTWARE.
// ----------------------------------------------------------------------------
#include <Eigen/Dense>
#include <iostream>
#include <memory>
#include "open3d/Open3D.h"
using namespace open3d;
std::tuple<std::shared_ptr<geometry::PointCloud>,
std::shared_ptr<geometry::PointCloud>,
std::shared_ptr<pipelines::registration::Feature>>
PreprocessPointCloud(const char *file_name, const float voxel_size) {
auto pcd = open3d::io::CreatePointCloudFromFile(file_name);
auto pcd_down = pcd->VoxelDownSample(voxel_size);
pcd_down->EstimateNormals(
open3d::geometry::KDTreeSearchParamHybrid(2 * voxel_size, 30));
auto pcd_fpfh = pipelines::registration::ComputeFPFHFeature(
*pcd_down,
open3d::geometry::KDTreeSearchParamHybrid(5 * voxel_size, 100));
return std::make_tuple(pcd, pcd_down, pcd_fpfh);
}
void VisualizeRegistration(const open3d::geometry::PointCloud &source,
const open3d::geometry::PointCloud &target,
const Eigen::Matrix4d &Transformation) {
std::shared_ptr<geometry::PointCloud> source_transformed_ptr(
new geometry::PointCloud);
std::shared_ptr<geometry::PointCloud> target_ptr(new geometry::PointCloud);
*source_transformed_ptr = source;
*target_ptr = target;
source_transformed_ptr->Transform(Transformation);
visualization::DrawGeometries({source_transformed_ptr, target_ptr},
"Registration result");
}
void PrintHelp() {
using namespace open3d;
PrintOpen3DVersion();
// clang-format off
utility::LogInfo("Usage:");
utility::LogInfo(" > RegistrationRANSAC source_pcd target_pcd"
"[--method=feature_matching] "
"[--voxel_size=0.05] [--distance_multiplier=1.5]"
"[--max_iterations 1000000] [--confidence 0.999]"
"[--mutual_filter]");
// clang-format on
}
int main(int argc, char *argv[]) {
using namespace open3d;
utility::SetVerbosityLevel(utility::VerbosityLevel::Debug);
if (argc < 3 ||
utility::ProgramOptionExistsAny(argc, argv, {"-h", "--help"})) {
PrintHelp();
return 1;
}
std::string method = "";
const std::string kMethodFeature = "feature_matching";
const std::string kMethodCorres = "correspondence";
if (utility::ProgramOptionExists(argc, argv, "--method")) {
method = utility::GetProgramOptionAsString(argc, argv, "--method");
} else {
method = "feature_matching";
}
if (method != kMethodFeature && method != kMethodCorres) {
utility::LogInfo(
"--method must be \'feature_matching\' or "
"\'correspondence\'.");
return 1;
}
bool mutual_filter = false;
if (utility::ProgramOptionExists(argc, argv, "--mutual_filter")) {
mutual_filter = true;
}
float voxel_size =
utility::GetProgramOptionAsDouble(argc, argv, "--voxel_size", 0.05);
float distance_multiplier = utility::GetProgramOptionAsDouble(
argc, argv, "--distance_multiplier", 1.5);
float distance_threshold = voxel_size * distance_multiplier;
int max_iterations = utility::GetProgramOptionAsInt(
argc, argv, "--max_iterations", 1000000);
float confidence = utility::GetProgramOptionAsDouble(argc, argv,
"--confidence", 0.999);
// Prepare input
std::shared_ptr<geometry::PointCloud> source, source_down, target,
target_down;
std::shared_ptr<pipelines::registration::Feature> source_fpfh, target_fpfh;
std::tie(source, source_down, source_fpfh) =
PreprocessPointCloud(argv[1], voxel_size);
std::tie(target, target_down, target_fpfh) =
PreprocessPointCloud(argv[2], voxel_size);
pipelines::registration::RegistrationResult registration_result;
// Prepare checkers
std::vector<std::reference_wrapper<
const pipelines::registration::CorrespondenceChecker>>
correspondence_checker;
auto correspondence_checker_edge_length =
pipelines::registration::CorrespondenceCheckerBasedOnEdgeLength(
0.9);
auto correspondence_checker_distance =
pipelines::registration::CorrespondenceCheckerBasedOnDistance(
distance_threshold);
correspondence_checker.push_back(correspondence_checker_edge_length);
correspondence_checker.push_back(correspondence_checker_distance);
if (method == kMethodFeature) {
registration_result = pipelines::registration::
RegistrationRANSACBasedOnFeatureMatching(
*source_down, *target_down, *source_fpfh, *target_fpfh,
mutual_filter, distance_threshold,
pipelines::registration::
TransformationEstimationPointToPoint(false),
3, correspondence_checker,
pipelines::registration::RANSACConvergenceCriteria(
max_iterations, confidence));
} else if (method == kMethodCorres) {
// Manually search correspondences
int nPti = int(source_down->points_.size());
int nPtj = int(target_down->points_.size());
geometry::KDTreeFlann feature_tree_i(*source_fpfh);
geometry::KDTreeFlann feature_tree_j(*target_fpfh);
pipelines::registration::CorrespondenceSet corres_ji;
std::vector<int> i_to_j(nPti, -1);
// Buffer all correspondences
for (int j = 0; j < nPtj; j++) {
std::vector<int> corres_tmp(1);
std::vector<double> dist_tmp(1);
feature_tree_i.SearchKNN(Eigen::VectorXd(target_fpfh->data_.col(j)),
1, corres_tmp, dist_tmp);
int i = corres_tmp[0];
corres_ji.push_back(Eigen::Vector2i(i, j));
}
if (mutual_filter) {
pipelines::registration::CorrespondenceSet mutual_corres;
for (auto &corres : corres_ji) {
int j = corres(1);
int j2i = corres(0);
std::vector<int> corres_tmp(1);
std::vector<double> dist_tmp(1);
feature_tree_j.SearchKNN(
Eigen::VectorXd(source_fpfh->data_.col(j2i)), 1,
corres_tmp, dist_tmp);
int i2j = corres_tmp[0];
if (i2j == j) {
mutual_corres.push_back(corres);
}
}
utility::LogDebug("{:d} points remain after mutual filter",
mutual_corres.size());
registration_result = pipelines::registration::
RegistrationRANSACBasedOnCorrespondence(
*source_down, *target_down, mutual_corres,
distance_threshold,
pipelines::registration::
TransformationEstimationPointToPoint(false),
3, correspondence_checker,
pipelines::registration::RANSACConvergenceCriteria(
max_iterations, confidence));
} else {
utility::LogDebug("{:d} points remain", corres_ji.size());
registration_result = pipelines::registration::
RegistrationRANSACBasedOnCorrespondence(
*source_down, *target_down, corres_ji,
distance_threshold,
pipelines::registration::
TransformationEstimationPointToPoint(false),
3, correspondence_checker,
pipelines::registration::RANSACConvergenceCriteria(
max_iterations, confidence));
}
}
VisualizeRegistration(*source, *target,
registration_result.transformation_);
return 0;
}
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