Tutorial: Hypothesis Verification for 3D Object Recognition
This tutorial aims at explaining how to do 3D object recognition in clutter by verifying model hypotheses in cluttered and heavily occluded 3D scenes. After descriptor matching, the tutorial runs one of the Correspondence Grouping algorithms available in PCL in order to cluster the set of point-to-point correspondences, determining instances of object hypotheses in the scene. On these hypotheses, the Global Hypothesis Verification algorithm is applied in order to decrease the amount of false positives.
Suggested readings and prerequisites
This tutorial is the follow-up of a previous tutorial on object recognition: 3D Object Recognition based on Correspondence Grouping To understand this tutorial, we suggest first to read and understand that tutorial.
More details on the Global Hypothesis Verification method can be found here: A. Aldoma, F. Tombari, L. Di Stefano, M. Vincze, A global hypothesis verification method for 3D object recognition, ECCV 2012
For more information on 3D Object Recognition in Clutter and on the standard feature-based recognition pipeline, we suggest this tutorial paper: A. Aldoma, Z.C. Marton, F. Tombari, W. Wohlkinger, C. Potthast, B. Zeisl, R.B. Rusu, S. Gedikli, M. Vincze, “Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation”, IEEE Robotics and Automation Magazine, 2012
The Code
Before starting, you should download from the GitHub folder: Correspondence Grouping the example PCD clouds used in this tutorial (milk.pcd and milk_cartoon_all_small_clorox.pcd), and place the files in the source older.
Then copy and paste the following code into your editor and save it as global_hypothesis_verification.cpp
.
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* Software License Agreement (BSD License)
*
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* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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#include <pcl/io/pcd_io.h>
#include <pcl/point_cloud.h>
#include <pcl/correspondence.h>
#include <pcl/features/normal_3d_omp.h>
#include <pcl/features/shot_omp.h>
#include <pcl/features/board.h>
#include <pcl/filters/uniform_sampling.h>
#include <pcl/recognition/cg/hough_3d.h>
#include <pcl/recognition/cg/geometric_consistency.h>
#include <pcl/recognition/hv/hv_go.h>
#include <pcl/registration/icp.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/kdtree/impl/kdtree_flann.hpp>
#include <pcl/common/transforms.h>
#include <pcl/console/parse.h>
typedef pcl::PointXYZRGBA PointType;
typedef pcl::Normal NormalType;
typedef pcl::ReferenceFrame RFType;
typedef pcl::SHOT352 DescriptorType;
struct CloudStyle
{
double r;
double g;
double b;
double size;
CloudStyle (double r,
double g,
double b,
double size) :
r (r),
g (g),
b (b),
size (size)
{
}
};
CloudStyle style_white (255.0, 255.0, 255.0, 4.0);
CloudStyle style_red (255.0, 0.0, 0.0, 3.0);
CloudStyle style_green (0.0, 255.0, 0.0, 5.0);
CloudStyle style_cyan (93.0, 200.0, 217.0, 4.0);
CloudStyle style_violet (255.0, 0.0, 255.0, 8.0);
std::string model_filename_;
std::string scene_filename_;
//Algorithm params
bool show_keypoints_ (false);
bool use_hough_ (true);
float model_ss_ (0.02f);
float scene_ss_ (0.02f);
float rf_rad_ (0.015f);
float descr_rad_ (0.02f);
float cg_size_ (0.01f);
float cg_thresh_ (5.0f);
int icp_max_iter_ (5);
float icp_corr_distance_ (0.005f);
float hv_resolution_ (0.005f);
float hv_occupancy_grid_resolution_ (0.01f);
float hv_clutter_reg_ (5.0f);
float hv_inlier_th_ (0.005f);
float hv_occlusion_th_ (0.01f);
float hv_rad_clutter_ (0.03f);
float hv_regularizer_ (3.0f);
float hv_rad_normals_ (0.05);
bool hv_detect_clutter_ (true);
/**
* Prints out Help message
* @param filename Runnable App Name
*/
void
showHelp (char *filename)
{
std::cout << std::endl;
std::cout << "***************************************************************************" << std::endl;
std::cout << "* *" << std::endl;
std::cout << "* Global Hypothese Verification Tutorial - Usage Guide *" << std::endl;
std::cout << "* *" << std::endl;
std::cout << "***************************************************************************" << std::endl << std::endl;
std::cout << "Usage: " << filename << " model_filename.pcd scene_filename.pcd [Options]" << std::endl << std::endl;
std::cout << "Options:" << std::endl;
std::cout << " -h: Show this help." << std::endl;
std::cout << " -k: Show keypoints." << std::endl;
std::cout << " --algorithm (Hough|GC): Clustering algorithm used (default Hough)." << std::endl;
std::cout << " --model_ss val: Model uniform sampling radius (default " << model_ss_ << ")" << std::endl;
std::cout << " --scene_ss val: Scene uniform sampling radius (default " << scene_ss_ << ")" << std::endl;
std::cout << " --rf_rad val: Reference frame radius (default " << rf_rad_ << ")" << std::endl;
std::cout << " --descr_rad val: Descriptor radius (default " << descr_rad_ << ")" << std::endl;
std::cout << " --cg_size val: Cluster size (default " << cg_size_ << ")" << std::endl;
std::cout << " --cg_thresh val: Clustering threshold (default " << cg_thresh_ << ")" << std::endl << std::endl;
std::cout << " --icp_max_iter val: ICP max iterations number (default " << icp_max_iter_ << ")" << std::endl;
std::cout << " --icp_corr_distance val: ICP correspondence distance (default " << icp_corr_distance_ << ")" << std::endl << std::endl;
std::cout << " --hv_clutter_reg val: Clutter Regularizer (default " << hv_clutter_reg_ << ")" << std::endl;
std::cout << " --hv_inlier_th val: Inlier threshold (default " << hv_inlier_th_ << ")" << std::endl;
std::cout << " --hv_occlusion_th val: Occlusion threshold (default " << hv_occlusion_th_ << ")" << std::endl;
std::cout << " --hv_rad_clutter val: Clutter radius (default " << hv_rad_clutter_ << ")" << std::endl;
std::cout << " --hv_regularizer val: Regularizer value (default " << hv_regularizer_ << ")" << std::endl;
std::cout << " --hv_rad_normals val: Normals radius (default " << hv_rad_normals_ << ")" << std::endl;
std::cout << " --hv_detect_clutter val: TRUE if clutter detect enabled (default " << hv_detect_clutter_ << ")" << std::endl << std::endl;
}
/**
* Parses Command Line Arguments (Argc,Argv)
* @param argc
* @param argv
*/
void
parseCommandLine (int argc,
char *argv[])
{
//Show help
if (pcl::console::find_switch (argc, argv, "-h"))
{
showHelp (argv[0]);
exit (0);
}
//Model & scene filenames
std::vector<int> filenames;
filenames = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");
if (filenames.size () != 2)
{
std::cout << "Filenames missing.\n";
showHelp (argv[0]);
exit (-1);
}
model_filename_ = argv[filenames[0]];
scene_filename_ = argv[filenames[1]];
//Program behavior
if (pcl::console::find_switch (argc, argv, "-k"))
{
show_keypoints_ = true;
}
std::string used_algorithm;
if (pcl::console::parse_argument (argc, argv, "--algorithm", used_algorithm) != -1)
{
if (used_algorithm.compare ("Hough") == 0)
{
use_hough_ = true;
}
else if (used_algorithm.compare ("GC") == 0)
{
use_hough_ = false;
}
else
{
std::cout << "Wrong algorithm name.\n";
showHelp (argv[0]);
exit (-1);
}
}
//General parameters
pcl::console::parse_argument (argc, argv, "--model_ss", model_ss_);
pcl::console::parse_argument (argc, argv, "--scene_ss", scene_ss_);
pcl::console::parse_argument (argc, argv, "--rf_rad", rf_rad_);
pcl::console::parse_argument (argc, argv, "--descr_rad", descr_rad_);
pcl::console::parse_argument (argc, argv, "--cg_size", cg_size_);
pcl::console::parse_argument (argc, argv, "--cg_thresh", cg_thresh_);
pcl::console::parse_argument (argc, argv, "--icp_max_iter", icp_max_iter_);
pcl::console::parse_argument (argc, argv, "--icp_corr_distance", icp_corr_distance_);
pcl::console::parse_argument (argc, argv, "--hv_clutter_reg", hv_clutter_reg_);
pcl::console::parse_argument (argc, argv, "--hv_inlier_th", hv_inlier_th_);
pcl::console::parse_argument (argc, argv, "--hv_occlusion_th", hv_occlusion_th_);
pcl::console::parse_argument (argc, argv, "--hv_rad_clutter", hv_rad_clutter_);
pcl::console::parse_argument (argc, argv, "--hv_regularizer", hv_regularizer_);
pcl::console::parse_argument (argc, argv, "--hv_rad_normals", hv_rad_normals_);
pcl::console::parse_argument (argc, argv, "--hv_detect_clutter", hv_detect_clutter_);
}
int
main (int argc,
char *argv[])
{
parseCommandLine (argc, argv);
pcl::PointCloud<PointType>::Ptr model (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr model_keypoints (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr scene (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr scene_keypoints (new pcl::PointCloud<PointType> ());
pcl::PointCloud<NormalType>::Ptr model_normals (new pcl::PointCloud<NormalType> ());
pcl::PointCloud<NormalType>::Ptr scene_normals (new pcl::PointCloud<NormalType> ());
pcl::PointCloud<DescriptorType>::Ptr model_descriptors (new pcl::PointCloud<DescriptorType> ());
pcl::PointCloud<DescriptorType>::Ptr scene_descriptors (new pcl::PointCloud<DescriptorType> ());
/**
* Load Clouds
*/
if (pcl::io::loadPCDFile (model_filename_, *model) < 0)
{
std::cout << "Error loading model cloud." << std::endl;
showHelp (argv[0]);
return (-1);
}
if (pcl::io::loadPCDFile (scene_filename_, *scene) < 0)
{
std::cout << "Error loading scene cloud." << std::endl;
showHelp (argv[0]);
return (-1);
}
/**
* Compute Normals
*/
pcl::NormalEstimationOMP<PointType, NormalType> norm_est;
norm_est.setKSearch (10);
norm_est.setInputCloud (model);
norm_est.compute (*model_normals);
norm_est.setInputCloud (scene);
norm_est.compute (*scene_normals);
/**
* Downsample Clouds to Extract keypoints
*/
pcl::UniformSampling<PointType> uniform_sampling;
uniform_sampling.setInputCloud (model);
uniform_sampling.setRadiusSearch (model_ss_);
uniform_sampling.filter (*model_keypoints);
std::cout << "Model total points: " << model->size () << "; Selected Keypoints: " << model_keypoints->size () << std::endl;
uniform_sampling.setInputCloud (scene);
uniform_sampling.setRadiusSearch (scene_ss_);
uniform_sampling.filter (*scene_keypoints);
std::cout << "Scene total points: " << scene->size () << "; Selected Keypoints: " << scene_keypoints->size () << std::endl;
/**
* Compute Descriptor for keypoints
*/
pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est;
descr_est.setRadiusSearch (descr_rad_);
descr_est.setInputCloud (model_keypoints);
descr_est.setInputNormals (model_normals);
descr_est.setSearchSurface (model);
descr_est.compute (*model_descriptors);
descr_est.setInputCloud (scene_keypoints);
descr_est.setInputNormals (scene_normals);
descr_est.setSearchSurface (scene);
descr_est.compute (*scene_descriptors);
/**
* Find Model-Scene Correspondences with KdTree
*/
pcl::CorrespondencesPtr model_scene_corrs (new pcl::Correspondences ());
pcl::KdTreeFLANN<DescriptorType> match_search;
match_search.setInputCloud (model_descriptors);
std::vector<int> model_good_keypoints_indices;
std::vector<int> scene_good_keypoints_indices;
for (std::size_t i = 0; i < scene_descriptors->size (); ++i)
{
std::vector<int> neigh_indices (1);
std::vector<float> neigh_sqr_dists (1);
if (!std::isfinite (scene_descriptors->at (i).descriptor[0])) //skipping NaNs
{
continue;
}
int found_neighs = match_search.nearestKSearch (scene_descriptors->at (i), 1, neigh_indices, neigh_sqr_dists);
if (found_neighs == 1 && neigh_sqr_dists[0] < 0.25f)
{
pcl::Correspondence corr (neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
model_scene_corrs->push_back (corr);
model_good_keypoints_indices.push_back (corr.index_query);
scene_good_keypoints_indices.push_back (corr.index_match);
}
}
pcl::PointCloud<PointType>::Ptr model_good_kp (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr scene_good_kp (new pcl::PointCloud<PointType> ());
pcl::copyPointCloud (*model_keypoints, model_good_keypoints_indices, *model_good_kp);
pcl::copyPointCloud (*scene_keypoints, scene_good_keypoints_indices, *scene_good_kp);
std::cout << "Correspondences found: " << model_scene_corrs->size () << std::endl;
/**
* Clustering
*/
std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
std::vector < pcl::Correspondences > clustered_corrs;
if (use_hough_)
{
pcl::PointCloud<RFType>::Ptr model_rf (new pcl::PointCloud<RFType> ());
pcl::PointCloud<RFType>::Ptr scene_rf (new pcl::PointCloud<RFType> ());
pcl::BOARDLocalReferenceFrameEstimation<PointType, NormalType, RFType> rf_est;
rf_est.setFindHoles (true);
rf_est.setRadiusSearch (rf_rad_);
rf_est.setInputCloud (model_keypoints);
rf_est.setInputNormals (model_normals);
rf_est.setSearchSurface (model);
rf_est.compute (*model_rf);
rf_est.setInputCloud (scene_keypoints);
rf_est.setInputNormals (scene_normals);
rf_est.setSearchSurface (scene);
rf_est.compute (*scene_rf);
// Clustering
pcl::Hough3DGrouping<PointType, PointType, RFType, RFType> clusterer;
clusterer.setHoughBinSize (cg_size_);
clusterer.setHoughThreshold (cg_thresh_);
clusterer.setUseInterpolation (true);
clusterer.setUseDistanceWeight (false);
clusterer.setInputCloud (model_keypoints);
clusterer.setInputRf (model_rf);
clusterer.setSceneCloud (scene_keypoints);
clusterer.setSceneRf (scene_rf);
clusterer.setModelSceneCorrespondences (model_scene_corrs);
clusterer.recognize (rototranslations, clustered_corrs);
}
else
{
pcl::GeometricConsistencyGrouping<PointType, PointType> gc_clusterer;
gc_clusterer.setGCSize (cg_size_);
gc_clusterer.setGCThreshold (cg_thresh_);
gc_clusterer.setInputCloud (model_keypoints);
gc_clusterer.setSceneCloud (scene_keypoints);
gc_clusterer.setModelSceneCorrespondences (model_scene_corrs);
gc_clusterer.recognize (rototranslations, clustered_corrs);
}
/**
* Stop if no instances
*/
if (rototranslations.size () <= 0)
{
std::cout << "*** No instances found! ***" << std::endl;
return (0);
}
else
{
std::cout << "Recognized Instances: " << rototranslations.size () << std::endl << std::endl;
}
/**
* Generates clouds for each instances found
*/
std::vector<pcl::PointCloud<PointType>::ConstPtr> instances;
for (std::size_t i = 0; i < rototranslations.size (); ++i)
{
pcl::PointCloud<PointType>::Ptr rotated_model (new pcl::PointCloud<PointType> ());
pcl::transformPointCloud (*model, *rotated_model, rototranslations[i]);
instances.push_back (rotated_model);
}
/**
* ICP
*/
std::vector<pcl::PointCloud<PointType>::ConstPtr> registered_instances;
if (true)
{
std::cout << "--- ICP ---------" << std::endl;
for (std::size_t i = 0; i < rototranslations.size (); ++i)
{
pcl::IterativeClosestPoint<PointType, PointType> icp;
icp.setMaximumIterations (icp_max_iter_);
icp.setMaxCorrespondenceDistance (icp_corr_distance_);
icp.setInputTarget (scene);
icp.setInputSource (instances[i]);
pcl::PointCloud<PointType>::Ptr registered (new pcl::PointCloud<PointType>);
icp.align (*registered);
registered_instances.push_back (registered);
std::cout << "Instance " << i << " ";
if (icp.hasConverged ())
{
std::cout << "Aligned!" << std::endl;
}
else
{
std::cout << "Not Aligned!" << std::endl;
}
}
std::cout << "-----------------" << std::endl << std::endl;
}
/**
* Hypothesis Verification
*/
std::cout << "--- Hypotheses Verification ---" << std::endl;
std::vector<bool> hypotheses_mask; // Mask Vector to identify positive hypotheses
pcl::GlobalHypothesesVerification<PointType, PointType> GoHv;
GoHv.setSceneCloud (scene); // Scene Cloud
GoHv.addModels (registered_instances, true); //Models to verify
GoHv.setResolution (hv_resolution_);
GoHv.setResolutionOccupancyGrid (hv_occupancy_grid_resolution_);
GoHv.setInlierThreshold (hv_inlier_th_);
GoHv.setOcclusionThreshold (hv_occlusion_th_);
GoHv.setRegularizer (hv_regularizer_);
GoHv.setRadiusClutter (hv_rad_clutter_);
GoHv.setClutterRegularizer (hv_clutter_reg_);
GoHv.setDetectClutter (hv_detect_clutter_);
GoHv.setRadiusNormals (hv_rad_normals_);
GoHv.verify ();
GoHv.getMask (hypotheses_mask); // i-element TRUE if hvModels[i] verifies hypotheses
for (int i = 0; i < hypotheses_mask.size (); i++)
{
if (hypotheses_mask[i])
{
std::cout << "Instance " << i << " is GOOD! <---" << std::endl;
}
else
{
std::cout << "Instance " << i << " is bad!" << std::endl;
}
}
std::cout << "-------------------------------" << std::endl;
/**
* Visualization
*/
pcl::visualization::PCLVisualizer viewer ("Hypotheses Verification");
viewer.addPointCloud (scene, "scene_cloud");
pcl::PointCloud<PointType>::Ptr off_scene_model (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr off_scene_model_keypoints (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr off_model_good_kp (new pcl::PointCloud<PointType> ());
pcl::transformPointCloud (*model, *off_scene_model, Eigen::Vector3f (-1, 0, 0), Eigen::Quaternionf (1, 0, 0, 0));
pcl::transformPointCloud (*model_keypoints, *off_scene_model_keypoints, Eigen::Vector3f (-1, 0, 0), Eigen::Quaternionf (1, 0, 0, 0));
pcl::transformPointCloud (*model_good_kp, *off_model_good_kp, Eigen::Vector3f (-1, 0, 0), Eigen::Quaternionf (1, 0, 0, 0));
if (show_keypoints_)
{
CloudStyle modelStyle = style_white;
pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_color_handler (off_scene_model, modelStyle.r, modelStyle.g, modelStyle.b);
viewer.addPointCloud (off_scene_model, off_scene_model_color_handler, "off_scene_model");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, modelStyle.size, "off_scene_model");
}
if (show_keypoints_)
{
CloudStyle goodKeypointStyle = style_violet;
pcl::visualization::PointCloudColorHandlerCustom<PointType> model_good_keypoints_color_handler (off_model_good_kp, goodKeypointStyle.r, goodKeypointStyle.g,
goodKeypointStyle.b);
viewer.addPointCloud (off_model_good_kp, model_good_keypoints_color_handler, "model_good_keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, goodKeypointStyle.size, "model_good_keypoints");
pcl::visualization::PointCloudColorHandlerCustom<PointType> scene_good_keypoints_color_handler (scene_good_kp, goodKeypointStyle.r, goodKeypointStyle.g,
goodKeypointStyle.b);
viewer.addPointCloud (scene_good_kp, scene_good_keypoints_color_handler, "scene_good_keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, goodKeypointStyle.size, "scene_good_keypoints");
}
for (std::size_t i = 0; i < instances.size (); ++i)
{
std::stringstream ss_instance;
ss_instance << "instance_" << i;
CloudStyle clusterStyle = style_red;
pcl::visualization::PointCloudColorHandlerCustom<PointType> instance_color_handler (instances[i], clusterStyle.r, clusterStyle.g, clusterStyle.b);
viewer.addPointCloud (instances[i], instance_color_handler, ss_instance.str ());
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, clusterStyle.size, ss_instance.str ());
CloudStyle registeredStyles = hypotheses_mask[i] ? style_green : style_cyan;
ss_instance << "_registered" << std::endl;
pcl::visualization::PointCloudColorHandlerCustom<PointType> registered_instance_color_handler (registered_instances[i], registeredStyles.r,
registeredStyles.g, registeredStyles.b);
viewer.addPointCloud (registered_instances[i], registered_instance_color_handler, ss_instance.str ());
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, registeredStyles.size, ss_instance.str ());
}
while (!viewer.wasStopped ())
{
viewer.spinOnce ();
}
return (0);
}
|
Walkthrough
Take a look at the various parts of the code to see how it works.
Input Parameters
bool hv_detect_clutter_ (true);
/**
* Prints out Help message
* @param filename Runnable App Name
*/
}
/**
* Parses Command Line Arguments (Argc,Argv)
* @param argc
* @param argv
*/
void
showHelp
function prints out the input parameters accepted by the program. parseCommandLine
binds the user input with program parameters.
The only two mandatory parameters are model_filename
and scene_filename
(all other parameters are initialized with a default value).
Other usefuls commands are:
--algorithm (Hough|GC)
used to switch clustering algorithm. See 3D Object Recognition based on Correspondence Grouping.-k
shows the keypoints used to compute the correspondences
Hypotheses Verification parameters are:
--hv_clutter_reg val: Clutter Regularizer (default 5.0)
--hv_inlier_th val: Inlier threshold (default 0.005)
--hv_occlusion_th val: Occlusion threshold (default 0.01)
--hv_rad_clutter val: Clutter radius (default 0.03)
--hv_regularizer val: Regularizer value (default 3.0)
--hv_rad_normals val: Normals radius (default 0.05)
--hv_detect_clutter val: TRUE if clutter detect enabled (default true)
More details on the Global Hypothesis Verification parameters can be found here: A. Aldoma, F. Tombari, L. Di Stefano, M. Vincze, A global hypothesis verification method for 3D object recognition, ECCV 2012.
Helpers
struct CloudStyle
{
double r;
double g;
double b;
double size;
CloudStyle (double r,
double g,
double b,
double size) :
r (r),
g (g),
b (b),
size (size)
{
}
};
CloudStyle style_white (255.0, 255.0, 255.0, 4.0);
CloudStyle style_red (255.0, 0.0, 0.0, 3.0);
CloudStyle style_green (0.0, 255.0, 0.0, 5.0);
CloudStyle style_cyan (93.0, 200.0, 217.0, 4.0);
CloudStyle style_violet (255.0, 0.0, 255.0, 8.0);
This simple struct is used to create Color presets for the clouds being visualized.
Clustering
The code below implements a full Clustering Pipeline: the input of the pipeline is a pair of point clouds (the model
and the scene
), and the output is
std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
rototraslations
represents a list of coarsely transformed models (“object hypotheses”) in the scene.
Take a look at the full pipeline:
}
/**
* Compute Normals
*/
pcl::NormalEstimationOMP<PointType, NormalType> norm_est;
norm_est.setKSearch (10);
norm_est.setInputCloud (model);
norm_est.compute (*model_normals);
norm_est.setInputCloud (scene);
norm_est.compute (*scene_normals);
/**
* Downsample Clouds to Extract keypoints
*/
pcl::UniformSampling<PointType> uniform_sampling;
uniform_sampling.setInputCloud (model);
uniform_sampling.setRadiusSearch (model_ss_);
uniform_sampling.filter (*model_keypoints);
std::cout << "Model total points: " << model->size () << "; Selected Keypoints: " << model_keypoints->size () << std::endl;
uniform_sampling.setInputCloud (scene);
uniform_sampling.setRadiusSearch (scene_ss_);
uniform_sampling.filter (*scene_keypoints);
std::cout << "Scene total points: " << scene->size () << "; Selected Keypoints: " << scene_keypoints->size () << std::endl;
/**
* Compute Descriptor for keypoints
*/
pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est;
descr_est.setRadiusSearch (descr_rad_);
descr_est.setInputCloud (model_keypoints);
descr_est.setInputNormals (model_normals);
descr_est.setSearchSurface (model);
descr_est.compute (*model_descriptors);
descr_est.setInputCloud (scene_keypoints);
descr_est.setInputNormals (scene_normals);
descr_est.setSearchSurface (scene);
descr_est.compute (*scene_descriptors);
/**
* Find Model-Scene Correspondences with KdTree
*/
pcl::CorrespondencesPtr model_scene_corrs (new pcl::Correspondences ());
pcl::KdTreeFLANN<DescriptorType> match_search;
match_search.setInputCloud (model_descriptors);
std::vector<int> model_good_keypoints_indices;
std::vector<int> scene_good_keypoints_indices;
for (std::size_t i = 0; i < scene_descriptors->size (); ++i)
{
std::vector<int> neigh_indices (1);
std::vector<float> neigh_sqr_dists (1);
if (!std::isfinite (scene_descriptors->at (i).descriptor[0])) //skipping NaNs
{
continue;
}
int found_neighs = match_search.nearestKSearch (scene_descriptors->at (i), 1, neigh_indices, neigh_sqr_dists);
if (found_neighs == 1 && neigh_sqr_dists[0] < 0.25f)
{
pcl::Correspondence corr (neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
model_scene_corrs->push_back (corr);
model_good_keypoints_indices.push_back (corr.index_query);
scene_good_keypoints_indices.push_back (corr.index_match);
}
}
pcl::PointCloud<PointType>::Ptr model_good_kp (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr scene_good_kp (new pcl::PointCloud<PointType> ());
pcl::copyPointCloud (*model_keypoints, model_good_keypoints_indices, *model_good_kp);
pcl::copyPointCloud (*scene_keypoints, scene_good_keypoints_indices, *scene_good_kp);
std::cout << "Correspondences found: " << model_scene_corrs->size () << std::endl;
/**
* Clustering
*/
std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
std::vector < pcl::Correspondences > clustered_corrs;
if (use_hough_)
{
pcl::PointCloud<RFType>::Ptr model_rf (new pcl::PointCloud<RFType> ());
pcl::PointCloud<RFType>::Ptr scene_rf (new pcl::PointCloud<RFType> ());
pcl::BOARDLocalReferenceFrameEstimation<PointType, NormalType, RFType> rf_est;
rf_est.setFindHoles (true);
rf_est.setRadiusSearch (rf_rad_);
rf_est.setInputCloud (model_keypoints);
rf_est.setInputNormals (model_normals);
rf_est.setSearchSurface (model);
rf_est.compute (*model_rf);
rf_est.setInputCloud (scene_keypoints);
rf_est.setInputNormals (scene_normals);
rf_est.setSearchSurface (scene);
rf_est.compute (*scene_rf);
// Clustering
pcl::Hough3DGrouping<PointType, PointType, RFType, RFType> clusterer;
clusterer.setHoughBinSize (cg_size_);
clusterer.setHoughThreshold (cg_thresh_);
clusterer.setUseInterpolation (true);
clusterer.setUseDistanceWeight (false);
clusterer.setInputCloud (model_keypoints);
clusterer.setInputRf (model_rf);
clusterer.setSceneCloud (scene_keypoints);
clusterer.setSceneRf (scene_rf);
clusterer.setModelSceneCorrespondences (model_scene_corrs);
clusterer.recognize (rototranslations, clustered_corrs);
}
else
{
pcl::GeometricConsistencyGrouping<PointType, PointType> gc_clusterer;
gc_clusterer.setGCSize (cg_size_);
gc_clusterer.setGCThreshold (cg_thresh_);
gc_clusterer.setInputCloud (model_keypoints);
gc_clusterer.setSceneCloud (scene_keypoints);
gc_clusterer.setModelSceneCorrespondences (model_scene_corrs);
gc_clusterer.recognize (rototranslations, clustered_corrs);
}
/**
For a full explanation of the above code see 3D Object Recognition based on Correspondence Grouping.
Model-in-Scene Projection
To improve the coarse transformation associated to each object hypothesis, we apply some ICP iterations.
We create a instances
list to store the “coarse” transformations :
*/
std::vector<pcl::PointCloud<PointType>::ConstPtr> instances;
for (std::size_t i = 0; i < rototranslations.size (); ++i)
{
pcl::PointCloud<PointType>::Ptr rotated_model (new pcl::PointCloud<PointType> ());
pcl::transformPointCloud (*model, *rotated_model, rototranslations[i]);
instances.push_back (rotated_model);
}
/**
then, we run ICP on the instances
wrt. the scene
to obtain the registered_instances
:
*/
std::vector<pcl::PointCloud<PointType>::ConstPtr> registered_instances;
if (true)
{
std::cout << "--- ICP ---------" << std::endl;
for (std::size_t i = 0; i < rototranslations.size (); ++i)
{
pcl::IterativeClosestPoint<PointType, PointType> icp;
icp.setMaximumIterations (icp_max_iter_);
icp.setMaxCorrespondenceDistance (icp_corr_distance_);
icp.setInputTarget (scene);
icp.setInputSource (instances[i]);
pcl::PointCloud<PointType>::Ptr registered (new pcl::PointCloud<PointType>);
icp.align (*registered);
registered_instances.push_back (registered);
std::cout << "Instance " << i << " ";
if (icp.hasConverged ())
{
std::cout << "Aligned!" << std::endl;
}
else
{
std::cout << "Not Aligned!" << std::endl;
}
}
std::cout << "-----------------" << std::endl << std::endl;
}
/**
Hypotheses Verification
*/
std::cout << "--- Hypotheses Verification ---" << std::endl;
std::vector<bool> hypotheses_mask; // Mask Vector to identify positive hypotheses
pcl::GlobalHypothesesVerification<PointType, PointType> GoHv;
GoHv.setSceneCloud (scene); // Scene Cloud
GoHv.addModels (registered_instances, true); //Models to verify
GoHv.setResolution (hv_resolution_);
GoHv.setResolutionOccupancyGrid (hv_occupancy_grid_resolution_);
GoHv.setInlierThreshold (hv_inlier_th_);
GoHv.setOcclusionThreshold (hv_occlusion_th_);
GoHv.setRegularizer (hv_regularizer_);
GoHv.setRadiusClutter (hv_rad_clutter_);
GoHv.setClutterRegularizer (hv_clutter_reg_);
GoHv.setDetectClutter (hv_detect_clutter_);
GoHv.setRadiusNormals (hv_rad_normals_);
GoHv.verify ();
GoHv.getMask (hypotheses_mask); // i-element TRUE if hvModels[i] verifies hypotheses
for (int i = 0; i < hypotheses_mask.size (); i++)
{
if (hypotheses_mask[i])
{
std::cout << "Instance " << i << " is GOOD! <---" << std::endl;
}
else
{
std::cout << "Instance " << i << " is bad!" << std::endl;
}
}
std::cout << "-------------------------------" << std::endl;
GlobalHypothesesVerification
takes as input a list of registered_instances
and a scene
so we can verify()
them
to get a hypotheses_mask
: this is a bool array where hypotheses_mask[i]
is TRUE
if registered_instances[i]
is a
verified hypothesis, FALSE
if it has been classified as a False Positive (hence, must be rejected).
Visualization
The first part of the Visualization code section is pretty simple, with -k
options the program displays goog keypoints in model and in scene
with a styleViolet
color.
Later we iterate on instances
, and each instances[i]
will be displayed in Viewer with a styleRed
color.
Each registered_instances[i]
will be displayed with two optional colors: styleGreen
if the current instance is verified (hypotheses_mask[i]
is TRUE
), styleCyan
otherwise.
* Visualization
*/
pcl::visualization::PCLVisualizer viewer ("Hypotheses Verification");
viewer.addPointCloud (scene, "scene_cloud");
pcl::PointCloud<PointType>::Ptr off_scene_model (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr off_scene_model_keypoints (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr off_model_good_kp (new pcl::PointCloud<PointType> ());
pcl::transformPointCloud (*model, *off_scene_model, Eigen::Vector3f (-1, 0, 0), Eigen::Quaternionf (1, 0, 0, 0));
pcl::transformPointCloud (*model_keypoints, *off_scene_model_keypoints, Eigen::Vector3f (-1, 0, 0), Eigen::Quaternionf (1, 0, 0, 0));
pcl::transformPointCloud (*model_good_kp, *off_model_good_kp, Eigen::Vector3f (-1, 0, 0), Eigen::Quaternionf (1, 0, 0, 0));
if (show_keypoints_)
{
CloudStyle modelStyle = style_white;
pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_color_handler (off_scene_model, modelStyle.r, modelStyle.g, modelStyle.b);
viewer.addPointCloud (off_scene_model, off_scene_model_color_handler, "off_scene_model");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, modelStyle.size, "off_scene_model");
}
if (show_keypoints_)
{
CloudStyle goodKeypointStyle = style_violet;
pcl::visualization::PointCloudColorHandlerCustom<PointType> model_good_keypoints_color_handler (off_model_good_kp, goodKeypointStyle.r, goodKeypointStyle.g,
goodKeypointStyle.b);
viewer.addPointCloud (off_model_good_kp, model_good_keypoints_color_handler, "model_good_keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, goodKeypointStyle.size, "model_good_keypoints");
pcl::visualization::PointCloudColorHandlerCustom<PointType> scene_good_keypoints_color_handler (scene_good_kp, goodKeypointStyle.r, goodKeypointStyle.g,
goodKeypointStyle.b);
viewer.addPointCloud (scene_good_kp, scene_good_keypoints_color_handler, "scene_good_keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, goodKeypointStyle.size, "scene_good_keypoints");
}
for (std::size_t i = 0; i < instances.size (); ++i)
{
std::stringstream ss_instance;
ss_instance << "instance_" << i;
CloudStyle clusterStyle = style_red;
pcl::visualization::PointCloudColorHandlerCustom<PointType> instance_color_handler (instances[i], clusterStyle.r, clusterStyle.g, clusterStyle.b);
viewer.addPointCloud (instances[i], instance_color_handler, ss_instance.str ());
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, clusterStyle.size, ss_instance.str ());
CloudStyle registeredStyles = hypotheses_mask[i] ? style_green : style_cyan;
ss_instance << "_registered" << std::endl;
pcl::visualization::PointCloudColorHandlerCustom<PointType> registered_instance_color_handler (registered_instances[i], registeredStyles.r,
registeredStyles.g, registeredStyles.b);
viewer.addPointCloud (registered_instances[i], registered_instance_color_handler, ss_instance.str ());
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, registeredStyles.size, ss_instance.str ());
}
while (!viewer.wasStopped ())
{
viewer.spinOnce ();
}
Compiling and running the program
Create a CMakeLists.txt
file and add the following lines into it:
1 2 3 4 5 6 7 8 9 10 11 12 13 | cmake_minimum_required(VERSION 2.6 FATAL_ERROR)
project(global_hypothesis_verification)
#Pcl
find_package(PCL 1.7 REQUIRED)
include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
add_executable (global_hypothesis_verification global_hypothesis_verification.cpp)
target_link_libraries (global_hypothesis_verification ${PCL_LIBRARIES})
|
After you have created the executable, you can then launch it following this example:
>>> ./global_hypothesis_verification milk.pcd milk_cartoon_all_small_clorox.pcd
Original Scene Image
You can simulate more false positives by using a larger bin size parameter for the Hough Voting Correspondence Grouping algorithm:
>>> ./global_hypothesis_verification milk.pcd milk_cartoon_all_small_clorox.pcd --cg_size 0.035