sift_keypoint.hpp
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#ifndef PCL_SIFT_KEYPOINT_IMPL_H_
#define PCL_SIFT_KEYPOINT_IMPL_H_
#include <pcl/keypoints/sift_keypoint.h>
#include <pcl/common/io.h>
#include <pcl/filters/voxel_grid.h>
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::SIFTKeypoint<PointInT, PointOutT>::setScales (float min_scale, int nr_octaves, int nr_scales_per_octave)
{
min_scale_ = min_scale;
nr_octaves_ = nr_octaves;
nr_scales_per_octave_ = nr_scales_per_octave;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::SIFTKeypoint<PointInT, PointOutT>::setMinimumContrast (float min_contrast)
{
min_contrast_ = min_contrast;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> bool
pcl::SIFTKeypoint<PointInT, PointOutT>::initCompute ()
{
if (min_scale_ <= 0)
{
PCL_ERROR ("[pcl::%s::initCompute] : Minimum scale (%f) must be strict positive!\n",
name_.c_str (), min_scale_);
return (false);
}
if (nr_octaves_ < 1)
{
PCL_ERROR ("[pcl::%s::initCompute] : Number of octaves (%d) must be at least 1!\n",
name_.c_str (), nr_octaves_);
return (false);
}
if (nr_scales_per_octave_ < 1)
{
PCL_ERROR ("[pcl::%s::initCompute] : Number of scales per octave (%d) must be at least 1!\n",
name_.c_str (), nr_scales_per_octave_);
return (false);
}
if (min_contrast_ < 0)
{
PCL_ERROR ("[pcl::%s::initCompute] : Minimum contrast (%f) must be non-negative!\n",
name_.c_str (), min_contrast_);
return (false);
}
this->setKSearch (1);
tree_.reset (new pcl::search::KdTree<PointInT> (true));
return (true);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::SIFTKeypoint<PointInT, PointOutT>::detectKeypoints (PointCloudOut &output)
{
if (surface_ && surface_ != input_)
{
PCL_WARN ("[pcl::%s::detectKeypoints] : ", name_.c_str ());
PCL_WARN ("A search surface has been set by setSearchSurface, but this SIFT keypoint detection algorithm does ");
PCL_WARN ("not support search surfaces other than the input cloud. ");
PCL_WARN ("The cloud provided in setInputCloud is being used instead.\n");
}
// Check if the output has a "scale" field
scale_idx_ = pcl::getFieldIndex<PointOutT> ("scale", out_fields_);
// Make sure the output cloud is empty
output.clear ();
// Create a local copy of the input cloud that will be resized for each octave
typename pcl::PointCloud<PointInT>::Ptr cloud (new pcl::PointCloud<PointInT> (*input_));
VoxelGrid<PointInT> voxel_grid;
// Search for keypoints at each octave
float scale = min_scale_;
for (int i_octave = 0; i_octave < nr_octaves_; ++i_octave)
{
// Downsample the point cloud
const float s = 1.0f * scale; // note: this can be adjusted
voxel_grid.setLeafSize (s, s, s);
voxel_grid.setInputCloud (cloud);
typename pcl::PointCloud<PointInT>::Ptr temp (new pcl::PointCloud<PointInT>);
voxel_grid.filter (*temp);
cloud = temp;
// Make sure the downsampled cloud still has enough points
const std::size_t min_nr_points = 25;
if (cloud->size () < min_nr_points)
break;
// Update the KdTree with the downsampled points
tree_->setInputCloud (cloud);
// Detect keypoints for the current scale
detectKeypointsForOctave (*cloud, *tree_, scale, nr_scales_per_octave_, output);
// Increase the scale by another octave
scale *= 2;
}
// Set final properties
output.height = 1;
output.width = output.size ();
output.header = input_->header;
output.sensor_origin_ = input_->sensor_origin_;
output.sensor_orientation_ = input_->sensor_orientation_;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::SIFTKeypoint<PointInT, PointOutT>::detectKeypointsForOctave (
const PointCloudIn &input, KdTree &tree, float base_scale, int nr_scales_per_octave,
PointCloudOut &output)
{
// Compute the difference of Gaussians (DoG) scale space
std::vector<float> scales (nr_scales_per_octave + 3);
for (int i_scale = 0; i_scale <= nr_scales_per_octave + 2; ++i_scale)
{
scales[i_scale] = base_scale * powf (2.0f, (1.0f * static_cast<float> (i_scale) - 1.0f) / static_cast<float> (nr_scales_per_octave));
}
Eigen::MatrixXf diff_of_gauss;
computeScaleSpace (input, tree, scales, diff_of_gauss);
// Find extrema in the DoG scale space
pcl::Indices extrema_indices;
std::vector<int> extrema_scales;
findScaleSpaceExtrema (input, tree, diff_of_gauss, extrema_indices, extrema_scales);
output.reserve (output.size () + extrema_indices.size ());
// Save scale?
if (scale_idx_ != -1)
{
// Add keypoints to output
for (std::size_t i_keypoint = 0; i_keypoint < extrema_indices.size (); ++i_keypoint)
{
PointOutT keypoint;
const auto &keypoint_index = extrema_indices[i_keypoint];
keypoint.x = input[keypoint_index].x;
keypoint.y = input[keypoint_index].y;
keypoint.z = input[keypoint_index].z;
memcpy (reinterpret_cast<char*> (&keypoint) + out_fields_[scale_idx_].offset,
&scales[extrema_scales[i_keypoint]], sizeof (float));
output.push_back (keypoint);
}
}
else
{
// Add keypoints to output
for (const auto &keypoint_index : extrema_indices)
{
PointOutT keypoint;
keypoint.x = input[keypoint_index].x;
keypoint.y = input[keypoint_index].y;
keypoint.z = input[keypoint_index].z;
output.push_back (keypoint);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT>
void pcl::SIFTKeypoint<PointInT, PointOutT>::computeScaleSpace (
const PointCloudIn &input, KdTree &tree, const std::vector<float> &scales,
Eigen::MatrixXf &diff_of_gauss)
{
diff_of_gauss.resize (input.size (), scales.size () - 1);
// For efficiency, we will only filter over points within 3 standard deviations
const float max_radius = 3.0f * scales.back ();
for (int i_point = 0; i_point < static_cast<int> (input.size ()); ++i_point)
{
pcl::Indices nn_indices;
std::vector<float> nn_dist;
tree.radiusSearch (i_point, max_radius, nn_indices, nn_dist); // *
// * note: at this stage of the algorithm, we must find all points within a radius defined by the maximum scale,
// regardless of the configurable search method specified by the user, so we directly employ tree.radiusSearch
// here instead of using searchForNeighbors.
// For each scale, compute the Gaussian "filter response" at the current point
float filter_response = 0.0f;
for (std::size_t i_scale = 0; i_scale < scales.size (); ++i_scale)
{
float sigma_sqr = powf (scales[i_scale], 2.0f);
float numerator = 0.0f;
float denominator = 0.0f;
for (std::size_t i_neighbor = 0; i_neighbor < nn_indices.size (); ++i_neighbor)
{
const float &value = getFieldValue_ (input[nn_indices[i_neighbor]]);
const float &dist_sqr = nn_dist[i_neighbor];
if (dist_sqr <= 9*sigma_sqr)
{
float w = std::exp (-0.5f * dist_sqr / sigma_sqr);
numerator += value * w;
denominator += w;
}
else break; // i.e. if dist > 3 standard deviations, then terminate early
}
float previous_filter_response = filter_response;
filter_response = numerator / denominator;
// Compute the difference between adjacent scales
if (i_scale > 0)
diff_of_gauss (i_point, i_scale - 1) = filter_response - previous_filter_response;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::SIFTKeypoint<PointInT, PointOutT>::findScaleSpaceExtrema (
const PointCloudIn &input, KdTree &tree, const Eigen::MatrixXf &diff_of_gauss,
pcl::Indices &extrema_indices, std::vector<int> &extrema_scales)
{
const int k = 25;
pcl::Indices nn_indices (k);
std::vector<float> nn_dist (k);
const int nr_scales = static_cast<int> (diff_of_gauss.cols ());
std::vector<float> min_val (nr_scales), max_val (nr_scales);
for (int i_point = 0; i_point < static_cast<int> (input.size ()); ++i_point)
{
// Define the local neighborhood around the current point
const std::size_t nr_nn = tree.nearestKSearch (i_point, k, nn_indices, nn_dist); //*
// * note: the neighborhood for finding local extrema is best defined as a small fixed-k neighborhood, regardless of
// the configurable search method specified by the user, so we directly employ tree.nearestKSearch here instead
// of using searchForNeighbors
// At each scale, find the extreme values of the DoG within the current neighborhood
for (int i_scale = 0; i_scale < nr_scales; ++i_scale)
{
min_val[i_scale] = std::numeric_limits<float>::max ();
max_val[i_scale] = -std::numeric_limits<float>::max ();
for (std::size_t i_neighbor = 0; i_neighbor < nr_nn; ++i_neighbor)
{
const float &d = diff_of_gauss (nn_indices[i_neighbor], i_scale);
min_val[i_scale] = (std::min) (min_val[i_scale], d);
max_val[i_scale] = (std::max) (max_val[i_scale], d);
}
}
// If the current point is an extreme value with high enough contrast, add it as a keypoint
for (int i_scale = 1; i_scale < nr_scales - 1; ++i_scale)
{
const float &val = diff_of_gauss (i_point, i_scale);
// Does the point have sufficient contrast?
if (std::abs (val) >= min_contrast_)
{
// Is it a local minimum?
if ((val == min_val[i_scale]) &&
(val < min_val[i_scale - 1]) &&
(val < min_val[i_scale + 1]))
{
extrema_indices.push_back (i_point);
extrema_scales.push_back (i_scale);
}
// Is it a local maximum?
else if ((val == max_val[i_scale]) &&
(val > max_val[i_scale - 1]) &&
(val > max_val[i_scale + 1]))
{
extrema_indices.push_back (i_point);
extrema_scales.push_back (i_scale);
}
}
}
}
}
#define PCL_INSTANTIATE_SIFTKeypoint(T,U) template class PCL_EXPORTS pcl::SIFTKeypoint<T,U>;
#endif // #ifndef PCL_SIFT_KEYPOINT_IMPL_H_
pcl::Filter::filter
void filter(PointCloud &output)
Calls the filtering method and returns the filtered dataset in output.
Definition: filter.h:121
pcl::KdTree
KdTree represents the base spatial locator class for kd-tree implementations.
Definition: kdtree.h:55
pcl::PCLBase::setInputCloud
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: pcl_base.hpp:65
pcl::PointCloud< PointInT >
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::SIFTKeypoint
SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset ...
Definition: sift_keypoint.h:94
pcl::SIFTKeypoint::initCompute
bool initCompute() override
Definition: sift_keypoint.hpp:64
pcl::SIFTKeypoint::setMinimumContrast
void setMinimumContrast(float min_contrast)
Provide a threshold to limit detection of keypoints without sufficient contrast.
Definition: sift_keypoint.hpp:57
pcl::SIFTKeypoint::setScales
void setScales(float min_scale, int nr_octaves, int nr_scales_per_octave)
Specify the range of scales over which to search for keypoints.
Definition: sift_keypoint.hpp:47
pcl::SIFTKeypoint::detectKeypoints
void detectKeypoints(PointCloudOut &output) override
Detect the SIFT keypoints for a set of points given in setInputCloud () using the spatial locator in ...
Definition: sift_keypoint.hpp:98
pcl::SIFTKeypoint::PointCloudOut
typename Keypoint< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: sift_keypoint.h:100
pcl::VoxelGrid
VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data.
Definition: voxel_grid.h:177
pcl::VoxelGrid::setLeafSize
void setLeafSize(const Eigen::Vector4f &leaf_size)
Set the voxel grid leaf size.
Definition: voxel_grid.h:221
pcl::search::KdTree
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:62
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133