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our_cvfh.hpp
/*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* $Id: cvfh.hpp 5311 2012-03-26 22:02:04Z aaldoma $
*
*/
#ifndef PCL_FEATURES_IMPL_OURCVFH_H_
#define PCL_FEATURES_IMPL_OURCVFH_H_
#include <pcl/features/our_cvfh.h>
#include <pcl/features/vfh.h>
#include <pcl/features/normal_3d.h>
#include <pcl/common/io.h> // for copyPointCloud
#include <pcl/common/common.h> // for getMaxDistance
#include <pcl/common/transforms.h>
#include <pcl/search/kdtree.h> // for KdTree
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT> void
pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::compute (PointCloudOut &output)
{
if (!Feature<PointInT, PointOutT>::initCompute ())
{
output.width = output.height = 0;
output.clear ();
return;
}
// Resize the output dataset
// Important! We should only allocate precisely how many elements we will need, otherwise
// we risk at pre-allocating too much memory which could lead to bad_alloc
// (see http://dev.pointclouds.org/issues/657)
output.width = output.height = 1;
output.resize (1);
// Perform the actual feature computation
computeFeature (output);
Feature<PointInT, PointOutT>::deinitCompute ();
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT> void
pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::extractEuclideanClustersSmooth (const pcl::PointCloud<pcl::PointNormal> &cloud,
const pcl::PointCloud<pcl::PointNormal> &normals,
float tolerance,
const pcl::search::Search<pcl::PointNormal>::Ptr &tree,
std::vector<pcl::PointIndices> &clusters, double eps_angle,
unsigned int min_pts_per_cluster,
unsigned int max_pts_per_cluster)
{
if (tree->getInputCloud ()->size () != cloud.size ())
{
PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point cloud "
"dataset (%zu) than the input cloud (%zu)!\n",
static_cast<std::size_t>(tree->getInputCloud()->size()),
static_cast<std::size_t>(cloud.size()));
return;
}
if (cloud.size () != normals.size ())
{
PCL_ERROR("[pcl::extractEuclideanClusters] Number of points in the input point "
"cloud (%zu) different than normals (%zu)!\n",
static_cast<std::size_t>(cloud.size()),
static_cast<std::size_t>(normals.size()));
return;
}
// If tree gives sorted results, we can skip the first one because it is the query point itself
const std::size_t nn_start_idx = tree->getSortedResults () ? 1 : 0;
// Create a bool vector of processed point indices, and initialize it to false
std::vector<bool> processed (cloud.size (), false);
pcl::Indices nn_indices;
std::vector<float> nn_distances;
// Process all points in the indices vector
for (std::size_t i = 0; i < cloud.size (); ++i)
{
if (processed[i])
continue;
std::vector<std::size_t> seed_queue;
std::size_t sq_idx = 0;
seed_queue.push_back (i);
processed[i] = true;
while (sq_idx < seed_queue.size ())
{
// Search for sq_idx
if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))
{
sq_idx++;
continue;
}
for (std::size_t j = nn_start_idx; j < nn_indices.size (); ++j)
{
if (processed[nn_indices[j]]) // Has this point been processed before ?
continue;
//processed[nn_indices[j]] = true;
// [-1;1]
double dot_p = normals[seed_queue[sq_idx]].normal[0] * normals[nn_indices[j]].normal[0]
+ normals[seed_queue[sq_idx]].normal[1] * normals[nn_indices[j]].normal[1] + normals[seed_queue[sq_idx]].normal[2]
* normals[nn_indices[j]].normal[2];
if (std::abs (std::acos (dot_p)) < eps_angle)
{
processed[nn_indices[j]] = true;
seed_queue.push_back (nn_indices[j]);
}
}
sq_idx++;
}
// If this queue is satisfactory, add to the clusters
if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
{
pcl::PointIndices r;
r.indices.resize (seed_queue.size ());
for (std::size_t j = 0; j < seed_queue.size (); ++j)
r.indices[j] = seed_queue[j];
std::sort (r.indices.begin (), r.indices.end ());
r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
r.header = cloud.header;
clusters.push_back (r); // We could avoid a copy by working directly in the vector
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT> void
pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::filterNormalsWithHighCurvature (const pcl::PointCloud<PointNT> & cloud,
pcl::Indices &indices_to_use,
pcl::Indices &indices_out, pcl::Indices &indices_in,
float threshold)
{
indices_out.resize (cloud.size ());
indices_in.resize (cloud.size ());
std::size_t in, out;
in = out = 0;
for (const auto &index : indices_to_use)
{
if (cloud[index].curvature > threshold)
{
indices_out[out] = index;
out++;
}
else
{
indices_in[in] = index;
in++;
}
}
indices_out.resize (out);
indices_in.resize (in);
}
template<typename PointInT, typename PointNT, typename PointOutT> bool
pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::sgurf (Eigen::Vector3f & centroid, Eigen::Vector3f & normal_centroid,
PointInTPtr & processed, std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > & transformations,
PointInTPtr & grid, pcl::PointIndices & indices)
{
Eigen::Vector3f plane_normal;
plane_normal[0] = -centroid[0];
plane_normal[1] = -centroid[1];
plane_normal[2] = -centroid[2];
Eigen::Vector3f z_vector = Eigen::Vector3f::UnitZ ();
plane_normal.normalize ();
Eigen::Vector3f axis = plane_normal.cross (z_vector);
double rotation = -asin (axis.norm ());
axis.normalize ();
Eigen::Affine3f transformPC (Eigen::AngleAxisf (static_cast<float> (rotation), axis));
grid->resize (processed->size ());
for (std::size_t k = 0; k < processed->size (); k++)
(*grid)[k].getVector4fMap () = (*processed)[k].getVector4fMap ();
pcl::transformPointCloud (*grid, *grid, transformPC);
Eigen::Vector4f centroid4f (centroid[0], centroid[1], centroid[2], 0);
Eigen::Vector4f normal_centroid4f (normal_centroid[0], normal_centroid[1], normal_centroid[2], 0);
centroid4f = transformPC * centroid4f;
normal_centroid4f = transformPC * normal_centroid4f;
Eigen::Vector3f centroid3f (centroid4f[0], centroid4f[1], centroid4f[2]);
Eigen::Vector4f farthest_away;
pcl::getMaxDistance (*grid, indices.indices, centroid4f, farthest_away);
farthest_away[3] = 0;
float max_dist = (farthest_away - centroid4f).norm ();
pcl::demeanPointCloud (*grid, centroid4f, *grid);
Eigen::Matrix4f center_mat;
center_mat.setIdentity (4, 4);
center_mat (0, 3) = -centroid4f[0];
center_mat (1, 3) = -centroid4f[1];
center_mat (2, 3) = -centroid4f[2];
Eigen::Matrix3f scatter;
scatter.setZero ();
float sum_w = 0.f;
for (const auto &index : indices.indices)
{
Eigen::Vector3f pvector = (*grid)[index].getVector3fMap ();
float d_k = (pvector).norm ();
float w = (max_dist - d_k);
Eigen::Vector3f diff = (pvector);
Eigen::Matrix3f mat = diff * diff.transpose ();
scatter += mat * w;
sum_w += w;
}
scatter /= sum_w;
Eigen::JacobiSVD <Eigen::MatrixXf> svd (scatter, Eigen::ComputeFullV);
Eigen::Vector3f evx = svd.matrixV ().col (0);
Eigen::Vector3f evy = svd.matrixV ().col (1);
Eigen::Vector3f evz = svd.matrixV ().col (2);
Eigen::Vector3f evxminus = evx * -1;
Eigen::Vector3f evyminus = evy * -1;
Eigen::Vector3f evzminus = evz * -1;
float s_xplus, s_xminus, s_yplus, s_yminus;
s_xplus = s_xminus = s_yplus = s_yminus = 0.f;
//disambiguate rf using all points
for (const auto& point: grid->points)
{
Eigen::Vector3f pvector = point.getVector3fMap ();
float dist_x, dist_y;
dist_x = std::abs (evx.dot (pvector));
dist_y = std::abs (evy.dot (pvector));
if ((pvector).dot (evx) >= 0)
s_xplus += dist_x;
else
s_xminus += dist_x;
if ((pvector).dot (evy) >= 0)
s_yplus += dist_y;
else
s_yminus += dist_y;
}
if (s_xplus < s_xminus)
evx = evxminus;
if (s_yplus < s_yminus)
evy = evyminus;
//select the axis that could be disambiguated more easily
float fx, fy;
float max_x = static_cast<float> (std::max (s_xplus, s_xminus));
float min_x = static_cast<float> (std::min (s_xplus, s_xminus));
float max_y = static_cast<float> (std::max (s_yplus, s_yminus));
float min_y = static_cast<float> (std::min (s_yplus, s_yminus));
fx = (min_x / max_x);
fy = (min_y / max_y);
Eigen::Vector3f normal3f = Eigen::Vector3f (normal_centroid4f[0], normal_centroid4f[1], normal_centroid4f[2]);
if (normal3f.dot (evz) < 0)
evz = evzminus;
//if fx/y close to 1, it was hard to disambiguate
//what if both are equally easy or difficult to disambiguate, namely fy == fx or very close
float max_axis = std::max (fx, fy);
float min_axis = std::min (fx, fy);
if ((min_axis / max_axis) > axis_ratio_)
{
PCL_WARN ("Both axes are equally easy/difficult to disambiguate\n");
Eigen::Vector3f evy_copy = evy;
Eigen::Vector3f evxminus = evx * -1;
Eigen::Vector3f evyminus = evy * -1;
if (min_axis > min_axis_value_)
{
//combination of all possibilities
evy = evx.cross (evz);
Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
transformations.push_back (trans);
evx = evxminus;
evy = evx.cross (evz);
trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
transformations.push_back (trans);
evx = evy_copy;
evy = evx.cross (evz);
trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
transformations.push_back (trans);
evx = evyminus;
evy = evx.cross (evz);
trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
transformations.push_back (trans);
}
else
{
//1-st case (evx selected)
evy = evx.cross (evz);
Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
transformations.push_back (trans);
//2-nd case (evy selected)
evx = evy_copy;
evy = evx.cross (evz);
trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
transformations.push_back (trans);
}
}
else
{
if (fy < fx)
{
evx = evy;
fx = fy;
}
evy = evx.cross (evz);
Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);
transformations.push_back (trans);
}
return true;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT> void
pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::computeRFAndShapeDistribution (PointInTPtr & processed, PointCloudOut & output,
std::vector<pcl::PointIndices> & cluster_indices)
{
PointCloudOut ourcvfh_output;
cluster_axes_.clear ();
cluster_axes_.resize (centroids_dominant_orientations_.size ());
for (std::size_t i = 0; i < centroids_dominant_orientations_.size (); i++)
{
std::vector < Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > transformations;
PointInTPtr grid (new pcl::PointCloud<PointInT>);
sgurf (centroids_dominant_orientations_[i], dominant_normals_[i], processed, transformations, grid, cluster_indices[i]);
// Make a note of how many transformations correspond to each cluster
cluster_axes_[i] = transformations.size ();
for (const auto &transformation : transformations)
{
pcl::transformPointCloud (*processed, *grid, transformation);
transforms_.push_back (transformation);
valid_transforms_.push_back (true);
std::vector < Eigen::VectorXf > quadrants (8);
int size_hists = 13;
int num_hists = 8;
for (int k = 0; k < num_hists; k++)
quadrants[k].setZero (size_hists);
Eigen::Vector4f centroid_p;
centroid_p.setZero ();
Eigen::Vector4f max_pt;
pcl::getMaxDistance (*grid, centroid_p, max_pt);
max_pt[3] = 0;
double distance_normalization_factor = (centroid_p - max_pt).norm ();
float hist_incr;
if (normalize_bins_)
hist_incr = 100.0f / static_cast<float> (grid->size () - 1);
else
hist_incr = 1.0f;
float * weights = new float[num_hists];
float sigma = 0.01f; //1cm
float sigma_sq = sigma * sigma;
for (const auto& point: grid->points)
{
Eigen::Vector4f p = point.getVector4fMap ();
p[3] = 0.f;
float d = p.norm ();
//compute weight for all octants
float wx = 1.f - std::exp (-((p[0] * p[0]) / (2.f * sigma_sq))); //how is the weight distributed among two semi-cubes
float wy = 1.f - std::exp (-((p[1] * p[1]) / (2.f * sigma_sq)));
float wz = 1.f - std::exp (-((p[2] * p[2]) / (2.f * sigma_sq)));
//distribute the weights using the x-coordinate
if (p[0] >= 0)
{
for (std::size_t ii = 0; ii <= 3; ii++)
weights[ii] = 0.5f - wx * 0.5f;
for (std::size_t ii = 4; ii <= 7; ii++)
weights[ii] = 0.5f + wx * 0.5f;
}
else
{
for (std::size_t ii = 0; ii <= 3; ii++)
weights[ii] = 0.5f + wx * 0.5f;
for (std::size_t ii = 4; ii <= 7; ii++)
weights[ii] = 0.5f - wx * 0.5f;
}
//distribute the weights using the y-coordinate
if (p[1] >= 0)
{
for (std::size_t ii = 0; ii <= 1; ii++)
weights[ii] *= 0.5f - wy * 0.5f;
for (std::size_t ii = 4; ii <= 5; ii++)
weights[ii] *= 0.5f - wy * 0.5f;
for (std::size_t ii = 2; ii <= 3; ii++)
weights[ii] *= 0.5f + wy * 0.5f;
for (std::size_t ii = 6; ii <= 7; ii++)
weights[ii] *= 0.5f + wy * 0.5f;
}
else
{
for (std::size_t ii = 0; ii <= 1; ii++)
weights[ii] *= 0.5f + wy * 0.5f;
for (std::size_t ii = 4; ii <= 5; ii++)
weights[ii] *= 0.5f + wy * 0.5f;
for (std::size_t ii = 2; ii <= 3; ii++)
weights[ii] *= 0.5f - wy * 0.5f;
for (std::size_t ii = 6; ii <= 7; ii++)
weights[ii] *= 0.5f - wy * 0.5f;
}
//distribute the weights using the z-coordinate
if (p[2] >= 0)
{
for (std::size_t ii = 0; ii <= 7; ii += 2)
weights[ii] *= 0.5f - wz * 0.5f;
for (std::size_t ii = 1; ii <= 7; ii += 2)
weights[ii] *= 0.5f + wz * 0.5f;
}
else
{
for (std::size_t ii = 0; ii <= 7; ii += 2)
weights[ii] *= 0.5f + wz * 0.5f;
for (std::size_t ii = 1; ii <= 7; ii += 2)
weights[ii] *= 0.5f - wz * 0.5f;
}
int h_index = (d <= 0) ? 0 : std::ceil (size_hists * (d / distance_normalization_factor)) - 1;
/* from http://www.pcl-users.org/OUR-CVFH-problem-td4028436.html
h_index will be 13 when d is computed on the farthest away point.
adding the following after computing h_index fixes the problem:
*/
if(h_index > 12)
h_index = 12;
for (int j = 0; j < num_hists; j++)
quadrants[j][h_index] += hist_incr * weights[j];
}
//copy to the cvfh signature
PointCloudOut vfh_signature;
vfh_signature.resize (1);
vfh_signature.width = vfh_signature.height = 1;
for (int d = 0; d < 308; ++d)
vfh_signature[0].histogram[d] = output[i].histogram[d];
int pos = 45 * 3;
for (int k = 0; k < num_hists; k++)
{
for (int ii = 0; ii < size_hists; ii++, pos++)
{
vfh_signature[0].histogram[pos] = quadrants[k][ii];
}
}
ourcvfh_output.push_back (vfh_signature[0]);
ourcvfh_output.width = ourcvfh_output.size ();
delete[] weights;
}
}
if (!ourcvfh_output.empty ())
{
ourcvfh_output.height = 1;
}
output = ourcvfh_output;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT> void
pcl::OURCVFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
if (refine_clusters_ <= 0.f)
refine_clusters_ = 1.f;
// Check if input was set
if (!normals_)
{
PCL_ERROR ("[pcl::%s::computeFeature] No input dataset containing normals was given!\n", getClassName ().c_str ());
output.width = output.height = 0;
output.clear ();
return;
}
if (normals_->size () != surface_->size ())
{
PCL_ERROR ("[pcl::%s::computeFeature] The number of points in the input dataset differs from the number of points in the dataset containing the normals!\n", getClassName ().c_str ());
output.width = output.height = 0;
output.clear ();
return;
}
centroids_dominant_orientations_.clear ();
clusters_.clear ();
transforms_.clear ();
dominant_normals_.clear ();
// ---[ Step 0: remove normals with high curvature
pcl::Indices indices_out;
pcl::Indices indices_in;
filterNormalsWithHighCurvature (*normals_, *indices_, indices_out, indices_in, curv_threshold_);
pcl::PointCloud<pcl::PointNormal>::Ptr normals_filtered_cloud (new pcl::PointCloud<pcl::PointNormal> ());
normals_filtered_cloud->width = indices_in.size ();
normals_filtered_cloud->height = 1;
normals_filtered_cloud->points.resize (normals_filtered_cloud->width);
pcl::Indices indices_from_nfc_to_indices;
indices_from_nfc_to_indices.resize (indices_in.size ());
for (std::size_t i = 0; i < indices_in.size (); ++i)
{
(*normals_filtered_cloud)[i].x = (*surface_)[indices_in[i]].x;
(*normals_filtered_cloud)[i].y = (*surface_)[indices_in[i]].y;
(*normals_filtered_cloud)[i].z = (*surface_)[indices_in[i]].z;
//(*normals_filtered_cloud)[i].getNormalVector4fMap() = (*normals_)[indices_in[i]].getNormalVector4fMap();
indices_from_nfc_to_indices[i] = indices_in[i];
}
std::vector<pcl::PointIndices> clusters;
if (normals_filtered_cloud->size () >= min_points_)
{
//recompute normals and use them for clustering
{
KdTreePtr normals_tree_filtered (new pcl::search::KdTree<pcl::PointNormal> (false));
normals_tree_filtered->setInputCloud (normals_filtered_cloud);
pcl::NormalEstimation<PointNormal, PointNormal> n3d;
n3d.setRadiusSearch (radius_normals_);
n3d.setSearchMethod (normals_tree_filtered);
n3d.setInputCloud (normals_filtered_cloud);
n3d.compute (*normals_filtered_cloud);
}
KdTreePtr normals_tree (new pcl::search::KdTree<pcl::PointNormal> (false));
normals_tree->setInputCloud (normals_filtered_cloud);
extractEuclideanClustersSmooth (*normals_filtered_cloud, *normals_filtered_cloud, cluster_tolerance_, normals_tree, clusters,
eps_angle_threshold_, static_cast<unsigned int> (min_points_));
std::vector<pcl::PointIndices> clusters_filtered;
int cluster_filtered_idx = 0;
for (const auto &cluster : clusters)
{
pcl::PointIndices pi;
pcl::PointIndices pi_filtered;
clusters_.push_back (pi);
clusters_filtered.push_back (pi_filtered);
Eigen::Vector4f avg_normal = Eigen::Vector4f::Zero ();
Eigen::Vector4f avg_centroid = Eigen::Vector4f::Zero ();
for (const auto &index : cluster.indices)
{
avg_normal += (*normals_filtered_cloud)[index].getNormalVector4fMap ();
avg_centroid += (*normals_filtered_cloud)[index].getVector4fMap ();
}
avg_normal /= static_cast<float> (cluster.indices.size ());
avg_centroid /= static_cast<float> (cluster.indices.size ());
avg_normal.normalize ();
Eigen::Vector3f avg_norm (avg_normal[0], avg_normal[1], avg_normal[2]);
Eigen::Vector3f avg_dominant_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);
for (const auto &index : cluster.indices)
{
//decide if normal should be added
double dot_p = avg_normal.dot ((*normals_filtered_cloud)[index].getNormalVector4fMap ());
if (std::abs (std::acos (dot_p)) < (eps_angle_threshold_ * refine_clusters_))
{
clusters_[cluster_filtered_idx].indices.push_back (indices_from_nfc_to_indices[index]);
clusters_filtered[cluster_filtered_idx].indices.push_back (index);
}
}
//remove last cluster if no points found...
if (clusters_[cluster_filtered_idx].indices.empty ())
{
clusters_.pop_back ();
clusters_filtered.pop_back ();
}
else
cluster_filtered_idx++;
}
clusters = clusters_filtered;
}
pcl::VFHEstimation<PointInT, PointNT, pcl::VFHSignature308> vfh;
vfh.setInputCloud (surface_);
vfh.setInputNormals (normals_);
vfh.setIndices (indices_);
vfh.setSearchMethod (this->tree_);
vfh.setUseGivenNormal (true);
vfh.setUseGivenCentroid (true);
vfh.setNormalizeBins (normalize_bins_);
output.height = 1;
// ---[ Step 1b : check if any dominant cluster was found
if (!clusters.empty ())
{ // ---[ Step 1b.1 : If yes, compute CVFH using the cluster information
for (const auto &cluster : clusters) //for each cluster
{
Eigen::Vector4f avg_normal = Eigen::Vector4f::Zero ();
Eigen::Vector4f avg_centroid = Eigen::Vector4f::Zero ();
for (const auto &index : cluster.indices)
{
avg_normal += (*normals_filtered_cloud)[index].getNormalVector4fMap ();
avg_centroid += (*normals_filtered_cloud)[index].getVector4fMap ();
}
avg_normal /= static_cast<float> (cluster.indices.size ());
avg_centroid /= static_cast<float> (cluster.indices.size ());
avg_normal.normalize ();
//append normal and centroid for the clusters
dominant_normals_.emplace_back (avg_normal[0], avg_normal[1], avg_normal[2]);
centroids_dominant_orientations_.emplace_back (avg_centroid[0], avg_centroid[1], avg_centroid[2]);
}
//compute modified VFH for all dominant clusters and add them to the list!
output.resize (dominant_normals_.size ());
output.width = dominant_normals_.size ();
for (std::size_t i = 0; i < dominant_normals_.size (); ++i)
{
//configure VFH computation for CVFH
vfh.setNormalToUse (dominant_normals_[i]);
vfh.setCentroidToUse (centroids_dominant_orientations_[i]);
pcl::PointCloud<pcl::VFHSignature308> vfh_signature;
vfh.compute (vfh_signature);
output[i] = vfh_signature[0];
}
//finish filling the descriptor with the shape distribution
PointInTPtr cloud_input (new pcl::PointCloud<PointInT>);
pcl::copyPointCloud (*surface_, *indices_, *cloud_input);
computeRFAndShapeDistribution (cloud_input, output, clusters_); //this will set transforms_
}
else
{ // ---[ Step 1b.1 : If no, compute a VFH using all the object points
PCL_WARN("No clusters were found in the surface... using VFH...\n");
Eigen::Vector4f avg_centroid;
pcl::compute3DCentroid (*surface_, avg_centroid);
Eigen::Vector3f cloud_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);
centroids_dominant_orientations_.push_back (cloud_centroid);
//configure VFH computation using all object points
vfh.setCentroidToUse (cloud_centroid);
vfh.setUseGivenNormal (false);
pcl::PointCloud<pcl::VFHSignature308> vfh_signature;
vfh.compute (vfh_signature);
output.resize (1);
output.width = 1;
output[0] = vfh_signature[0];
Eigen::Matrix4f id = Eigen::Matrix4f::Identity ();
transforms_.push_back (id);
valid_transforms_.push_back (false);
}
}
#define PCL_INSTANTIATE_OURCVFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::OURCVFHEstimation<T,NT,OutT>;
#endif // PCL_FEATURES_IMPL_OURCVFH_H_
pcl::FeatureFromNormals::setInputNormals
void setInputNormals(const PointCloudNConstPtr &normals)
Provide a pointer to the input dataset that contains the point normals of the XYZ dataset.
Definition feature.h:339
pcl::Feature
Feature represents the base feature class.
Definition feature.h:107
pcl::Feature::setRadiusSearch
void setRadiusSearch(double radius)
Set the sphere radius that is to be used for determining the nearest neighbors used for the feature e...
Definition feature.h:198
pcl::Feature::setSearchMethod
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
Definition feature.h:164
pcl::Feature::compute
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition feature.hpp:194
pcl::NormalEstimation
NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point.
Definition normal_3d.h:244
pcl::NormalEstimation::setInputCloud
void setInputCloud(const PointCloudConstPtr &cloud) override
Provide a pointer to the input dataset.
Definition normal_3d.h:328
pcl::OURCVFHEstimation
OURCVFHEstimation estimates the Oriented, Unique and Repetable Clustered Viewpoint Feature Histogram ...
Definition our_cvfh.h:60
pcl::OURCVFHEstimation::sgurf
bool sgurf(Eigen::Vector3f &centroid, Eigen::Vector3f &normal_centroid, PointInTPtr &processed, std::vector< Eigen::Matrix4f, Eigen::aligned_allocator< Eigen::Matrix4f > > &transformations, PointInTPtr &grid, pcl::PointIndices &indices)
Computes SGURF.
Definition our_cvfh.hpp:201
pcl::OURCVFHEstimation::PointCloudOut
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition our_cvfh.h:72
pcl::OURCVFHEstimation::filterNormalsWithHighCurvature
void filterNormalsWithHighCurvature(const pcl::PointCloud< PointNT > &cloud, pcl::Indices &indices_to_use, pcl::Indices &indices_out, pcl::Indices &indices_in, float threshold)
Removes normals with high curvature caused by real edges or noisy data.
Definition our_cvfh.hpp:171
pcl::OURCVFHEstimation::compute
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition our_cvfh.hpp:54
pcl::OURCVFHEstimation::PointInTPtr
typename pcl::PointCloud< PointInT >::Ptr PointInTPtr
Definition our_cvfh.h:74
pcl::OURCVFHEstimation::computeRFAndShapeDistribution
void computeRFAndShapeDistribution(PointInTPtr &processed, PointCloudOut &output, std::vector< pcl::PointIndices > &cluster_indices)
Computes SGURF and the shape distribution based on the selected SGURF.
Definition our_cvfh.hpp:384
pcl::PCLBase::setInputCloud
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition pcl_base.hpp:65
pcl::PCLBase::setIndices
virtual void setIndices(const IndicesPtr &indices)
Provide a pointer to the vector of indices that represents the input data.
Definition pcl_base.hpp:72
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PointCloud::push_back
void push_back(const PointT &pt)
Insert a new point in the cloud, at the end of the container.
Definition point_cloud.h:663
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition point_cloud.h:392
pcl::PointCloud::size
std::size_t size() const
Definition point_cloud.h:443
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition point_cloud.h:413
pcl::VFHEstimation
VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud data...
Definition vfh.h:73
pcl::VFHEstimation::setUseGivenNormal
void setUseGivenNormal(bool use)
Set use_given_normal_.
Definition vfh.h:142
pcl::VFHEstimation::setCentroidToUse
void setCentroidToUse(const Eigen::Vector3f &centroid)
Set centroid_to_use_.
Definition vfh.h:171
pcl::VFHEstimation::compute
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition vfh.hpp:65
pcl::VFHEstimation::setNormalToUse
void setNormalToUse(const Eigen::Vector3f &normal)
Set the normal to use.
Definition vfh.h:152
pcl::VFHEstimation::setNormalizeBins
void setNormalizeBins(bool normalize)
set normalize_bins_
Definition vfh.h:180
pcl::VFHEstimation::setUseGivenCentroid
void setUseGivenCentroid(bool use)
Set use_given_centroid_.
Definition vfh.h:161
pcl::search::KdTree
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition kdtree.h:62
pcl::search::Search::getSortedResults
virtual bool getSortedResults()
Gets whether the results should be sorted (ascending in the distance) or not Otherwise the results ma...
Definition search.hpp:68
pcl::search::Search::Ptr
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition search.h:81
pcl::search::Search::getInputCloud
virtual PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition search.h:124
pcl::search::Search::radiusSearch
virtual int radiusSearch(const PointT &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const =0
Search for all the nearest neighbors of the query point in a given radius.
common.h
Define standard C methods and C++ classes that are common to all methods.
pcl::getMaxDistance
void getMaxDistance(const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
Get the point at maximum distance from a given point and a given pointcloud.
Definition common.hpp:197
pcl::demeanPointCloud
void demeanPointCloud(ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Matrix< Scalar, 4, 1 > &centroid, pcl::PointCloud< PointT > &cloud_out, int npts=0)
Subtract a centroid from a point cloud and return the de-meaned representation.
Definition centroid.hpp:933
pcl::transformPointCloud
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform, bool copy_all_fields)
Apply a rigid transform defined by a 4x4 matrix.
Definition transforms.hpp:221
pcl::compute3DCentroid
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition centroid.hpp:57
pcl::copyPointCloud
void copyPointCloud(const pcl::PointCloud< PointInT > &cloud_in, pcl::PointCloud< PointOutT > &cloud_out)
Copy all the fields from a given point cloud into a new point cloud.
Definition io.hpp:142
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl::PointIndices
Definition PointIndices.h:12
pcl::PointIndices::header
::pcl::PCLHeader header
Definition PointIndices.h:18
pcl::PointIndices::indices
Indices indices
Definition PointIndices.h:20