Point Cloud Library (PCL) 1.13.0
intensity_spin.hpp
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40
41#ifndef PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
42#define PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
43
44#include <pcl/features/intensity_spin.h>
45
46//////////////////////////////////////////////////////////////////////////////////////////////
47template <typename PointInT, typename PointOutT> void
49 const PointCloudIn &cloud, float radius, float sigma,
50 int k,
51 const pcl::Indices &indices,
52 const std::vector<float> &squared_distances,
53 Eigen::MatrixXf &intensity_spin_image)
54{
55 // Determine the number of bins to use based on the size of intensity_spin_image
56 int nr_distance_bins = static_cast<int> (intensity_spin_image.cols ());
57 int nr_intensity_bins = static_cast<int> (intensity_spin_image.rows ());
58
59 // Find the min and max intensity values in the given neighborhood
60 float min_intensity = std::numeric_limits<float>::max ();
61 float max_intensity = -std::numeric_limits<float>::max ();
62 for (int idx = 0; idx < k; ++idx)
63 {
64 min_intensity = (std::min) (min_intensity, cloud[indices[idx]].intensity);
65 max_intensity = (std::max) (max_intensity, cloud[indices[idx]].intensity);
66 }
67
68 float constant = 1.0f / (2.0f * sigma_ * sigma_);
69 // Compute the intensity spin image
70 intensity_spin_image.setZero ();
71 for (int idx = 0; idx < k; ++idx)
72 {
73 // Normalize distance and intensity values to: 0.0 <= d,i < nr_distance_bins,nr_intensity_bins
74 const float eps = std::numeric_limits<float>::epsilon ();
75 float d = static_cast<float> (nr_distance_bins) * std::sqrt (squared_distances[idx]) / (radius + eps);
76 float i = static_cast<float> (nr_intensity_bins) *
77 (cloud[indices[idx]].intensity - min_intensity) / (max_intensity - min_intensity + eps);
78
79 if (sigma == 0)
80 {
81 // If sigma is zero, update the histogram with no smoothing kernel
82 int d_idx = static_cast<int> (d);
83 int i_idx = static_cast<int> (i);
84 intensity_spin_image (i_idx, d_idx) += 1;
85 }
86 else
87 {
88 // Compute the bin indices that need to be updated (+/- 3 standard deviations)
89 int d_idx_min = (std::max)(static_cast<int> (std::floor (d - 3*sigma)), 0);
90 int d_idx_max = (std::min)(static_cast<int> (std::ceil (d + 3*sigma)), nr_distance_bins - 1);
91 int i_idx_min = (std::max)(static_cast<int> (std::floor (i - 3*sigma)), 0);
92 int i_idx_max = (std::min)(static_cast<int> (std::ceil (i + 3*sigma)), nr_intensity_bins - 1);
93
94 // Update the appropriate bins of the histogram
95 for (int i_idx = i_idx_min; i_idx <= i_idx_max; ++i_idx)
96 {
97 for (int d_idx = d_idx_min; d_idx <= d_idx_max; ++d_idx)
98 {
99 // Compute a "soft" update weight based on the distance between the point and the bin
100 float w = std::exp (-powf (d - static_cast<float> (d_idx), 2.0f) * constant - powf (i - static_cast<float> (i_idx), 2.0f) * constant);
101 intensity_spin_image (i_idx, d_idx) += w;
102 }
103 }
104 }
105 }
106}
107
108//////////////////////////////////////////////////////////////////////////////////////////////
109template <typename PointInT, typename PointOutT> void
111{
112 // Make sure a search radius is set
113 if (search_radius_ == 0.0)
114 {
115 PCL_ERROR ("[pcl::%s::computeFeature] The search radius must be set before computing the feature!\n",
116 getClassName ().c_str ());
117 output.width = output.height = 0;
118 output.clear ();
119 return;
120 }
121
122 // Make sure the spin image has valid dimensions
123 if (nr_intensity_bins_ <= 0)
124 {
125 PCL_ERROR ("[pcl::%s::computeFeature] The number of intensity bins must be greater than zero!\n",
126 getClassName ().c_str ());
127 output.width = output.height = 0;
128 output.clear ();
129 return;
130 }
131 if (nr_distance_bins_ <= 0)
132 {
133 PCL_ERROR ("[pcl::%s::computeFeature] The number of distance bins must be greater than zero!\n",
134 getClassName ().c_str ());
135 output.width = output.height = 0;
136 output.clear ();
137 return;
138 }
139
140 Eigen::MatrixXf intensity_spin_image (nr_intensity_bins_, nr_distance_bins_);
141 // Allocate enough space to hold the radiusSearch results
142 pcl::Indices nn_indices (surface_->size ());
143 std::vector<float> nn_dist_sqr (surface_->size ());
144
145 output.is_dense = true;
146 // Iterating over the entire index vector
147 for (std::size_t idx = 0; idx < indices_->size (); ++idx)
148 {
149 // Find neighbors within the search radius
150 // TODO: do we want to use searchForNeigbors instead?
151 int k = tree_->radiusSearch ((*indices_)[idx], search_radius_, nn_indices, nn_dist_sqr);
152 if (k == 0)
153 {
154 for (int bin = 0; bin < nr_intensity_bins_ * nr_distance_bins_; ++bin)
155 output[idx].histogram[bin] = std::numeric_limits<float>::quiet_NaN ();
156 output.is_dense = false;
157 continue;
158 }
159
160 // Compute the intensity spin image
161 computeIntensitySpinImage (*surface_, static_cast<float> (search_radius_), sigma_, k, nn_indices, nn_dist_sqr, intensity_spin_image);
162
163 // Copy into the resultant cloud
164 std::size_t bin = 0;
165 for (Eigen::Index bin_j = 0; bin_j < intensity_spin_image.cols (); ++bin_j)
166 for (Eigen::Index bin_i = 0; bin_i < intensity_spin_image.rows (); ++bin_i)
167 output[idx].histogram[bin++] = intensity_spin_image (bin_i, bin_j);
168 }
169}
170
171#define PCL_INSTANTIATE_IntensitySpinEstimation(T,NT) template class PCL_EXPORTS pcl::IntensitySpinEstimation<T,NT>;
172
173#endif // PCL_FEATURES_IMPL_INTENSITY_SPIN_H_
174
void computeFeature(PointCloudOut &output) override
Estimate the intensity-domain descriptors at a set of points given by <setInputCloud (),...
void computeIntensitySpinImage(const PointCloudIn &cloud, float radius, float sigma, int k, const pcl::Indices &indices, const std::vector< float > &squared_distances, Eigen::MatrixXf &intensity_spin_image)
Estimate the intensity-domain spin image descriptor for a given point based on its spatial neighborho...
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:403
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:398
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:400
void clear()
Removes all points in a cloud and sets the width and height to 0.
Definition: point_cloud.h:885
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133