Point Cloud Library (PCL) 1.12.1
multiscale_feature_persistence.hpp
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39
40#ifndef PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_
41#define PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_
42
43#include <pcl/features/multiscale_feature_persistence.h>
44
45//////////////////////////////////////////////////////////////////////////////////////////////
46template <typename PointSource, typename PointFeature>
48 alpha_ (0),
49 distance_metric_ (L1),
50 feature_estimator_ (),
51 features_at_scale_ (),
52 feature_representation_ ()
53{
54 feature_representation_.reset (new DefaultPointRepresentation<PointFeature>);
55 // No input is needed, hack around the initCompute () check from PCLBase
57}
58
59
60//////////////////////////////////////////////////////////////////////////////////////////////
61template <typename PointSource, typename PointFeature> bool
63{
65 {
66 PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] PCLBase::initCompute () failed - no input cloud was given.\n");
67 return false;
68 }
69 if (!feature_estimator_)
70 {
71 PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] No feature estimator was set\n");
72 return false;
73 }
74 if (scale_values_.empty ())
75 {
76 PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] No scale values were given\n");
77 return false;
78 }
79
80 mean_feature_.resize (feature_representation_->getNumberOfDimensions ());
81
82 return true;
83}
84
85
86//////////////////////////////////////////////////////////////////////////////////////////////
87template <typename PointSource, typename PointFeature> void
89{
90 features_at_scale_.clear ();
91 features_at_scale_.reserve (scale_values_.size ());
92 features_at_scale_vectorized_.clear ();
93 features_at_scale_vectorized_.reserve (scale_values_.size ());
94 for (std::size_t scale_i = 0; scale_i < scale_values_.size (); ++scale_i)
95 {
96 FeatureCloudPtr feature_cloud (new FeatureCloud ());
97 computeFeatureAtScale (scale_values_[scale_i], feature_cloud);
98 features_at_scale_[scale_i] = feature_cloud;
99
100 // Vectorize each feature and insert it into the vectorized feature storage
101 std::vector<std::vector<float> > feature_cloud_vectorized;
102 feature_cloud_vectorized.reserve (feature_cloud->size ());
103
104 for (const auto& feature: feature_cloud->points)
105 {
106 std::vector<float> feature_vectorized (feature_representation_->getNumberOfDimensions ());
107 feature_representation_->vectorize (feature, feature_vectorized);
108 feature_cloud_vectorized.emplace_back (std::move(feature_vectorized));
109 }
110 features_at_scale_vectorized_.emplace_back (std::move(feature_cloud_vectorized));
111 }
112}
113
114
115//////////////////////////////////////////////////////////////////////////////////////////////
116template <typename PointSource, typename PointFeature> void
118 FeatureCloudPtr &features)
119{
120 feature_estimator_->setRadiusSearch (scale);
121 feature_estimator_->compute (*features);
122}
123
124
125//////////////////////////////////////////////////////////////////////////////////////////////
126template <typename PointSource, typename PointFeature> float
128 const std::vector<float> &b)
129{
130 return (pcl::selectNorm<std::vector<float> > (a, b, a.size (), distance_metric_));
131}
132
133
134//////////////////////////////////////////////////////////////////////////////////////////////
135template <typename PointSource, typename PointFeature> void
137{
138 // Reset mean feature
139 std::fill_n(mean_feature_.begin (), mean_feature_.size (), 0.f);
140
141 std::size_t normalization_factor = 0;
142 for (const auto& scale: features_at_scale_vectorized_)
143 {
144 normalization_factor += scale.size (); // not using accumulate for cache efficiency
145 for (const auto &feature : scale)
146 std::transform(mean_feature_.cbegin (), mean_feature_.cend (),
147 feature.cbegin (), mean_feature_.begin (), std::plus<>{});
148 }
149
150 const float factor = std::min<float>(1, normalization_factor);
151 std::transform(mean_feature_.cbegin(),
152 mean_feature_.cend(),
153 mean_feature_.begin(),
154 [factor](const auto& mean) {
155 return mean / factor;
156 });
157}
158
159
160//////////////////////////////////////////////////////////////////////////////////////////////
161template <typename PointSource, typename PointFeature> void
163{
164 unique_features_indices_.clear ();
165 unique_features_table_.clear ();
166 unique_features_indices_.reserve (scale_values_.size ());
167 unique_features_table_.reserve (scale_values_.size ());
168
169 for (std::size_t scale_i = 0; scale_i < features_at_scale_vectorized_.size (); ++scale_i)
170 {
171 // Calculate standard deviation within the scale
172 float standard_dev = 0.0;
173 std::vector<float> diff_vector (features_at_scale_vectorized_[scale_i].size ());
174 diff_vector.clear();
175
176 for (const auto& feature: features_at_scale_vectorized_[scale_i])
177 {
178 float diff = distanceBetweenFeatures (feature, mean_feature_);
179 standard_dev += diff * diff;
180 diff_vector.emplace_back (diff);
181 }
182 standard_dev = std::sqrt (standard_dev / static_cast<float> (features_at_scale_vectorized_[scale_i].size ()));
183 PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::extractUniqueFeatures] Standard deviation for scale %f is %f\n", scale_values_[scale_i], standard_dev);
184
185 // Select only points outside (mean +/- alpha * standard_dev)
186 std::list<std::size_t> indices_per_scale;
187 std::vector<bool> indices_table_per_scale (features_at_scale_[scale_i]->size (), false);
188 for (std::size_t point_i = 0; point_i < features_at_scale_[scale_i]->size (); ++point_i)
189 {
190 if (diff_vector[point_i] > alpha_ * standard_dev)
191 {
192 indices_per_scale.emplace_back (point_i);
193 indices_table_per_scale[point_i] = true;
194 }
195 }
196 unique_features_indices_.emplace_back (std::move(indices_per_scale));
197 unique_features_table_.emplace_back (std::move(indices_table_per_scale));
198 }
199}
200
201
202//////////////////////////////////////////////////////////////////////////////////////////////
203template <typename PointSource, typename PointFeature> void
205 pcl::IndicesPtr &output_indices)
206{
207 if (!initCompute ())
208 return;
209
210 // Compute the features for all scales with the given feature estimator
211 PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Computing features ...\n");
212 computeFeaturesAtAllScales ();
213
214 // Compute mean feature
215 PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Calculating mean feature ...\n");
216 calculateMeanFeature ();
217
218 // Get the 'unique' features at each scale
219 PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Extracting unique features ...\n");
220 extractUniqueFeatures ();
221
222 PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Determining persistent features between scales ...\n");
223 // Determine persistent features between scales
224
225/*
226 // Method 1: a feature is considered persistent if it is 'unique' in at least 2 different scales
227 for (std::size_t scale_i = 0; scale_i < features_at_scale_vectorized_.size () - 1; ++scale_i)
228 for (std::list<std::size_t>::iterator feature_it = unique_features_indices_[scale_i].begin (); feature_it != unique_features_indices_[scale_i].end (); ++feature_it)
229 {
230 if (unique_features_table_[scale_i][*feature_it] == true)
231 {
232 output_features.push_back ((*features_at_scale_[scale_i])[*feature_it]);
233 output_indices->push_back (feature_estimator_->getIndices ()->at (*feature_it));
234 }
235 }
236*/
237 // Method 2: a feature is considered persistent if it is 'unique' in all the scales
238 for (const auto& feature: unique_features_indices_.front ())
239 {
240 bool present_in_all = true;
241 for (std::size_t scale_i = 0; scale_i < features_at_scale_.size (); ++scale_i)
242 present_in_all = present_in_all && unique_features_table_[scale_i][feature];
243
244 if (present_in_all)
245 {
246 output_features.emplace_back ((*features_at_scale_.front ())[feature]);
247 output_indices->emplace_back (feature_estimator_->getIndices ()->at (feature));
248 }
249 }
250
251 // Consider that output cloud is unorganized
252 output_features.header = feature_estimator_->getInputCloud ()->header;
253 output_features.is_dense = feature_estimator_->getInputCloud ()->is_dense;
254 output_features.width = output_features.size ();
255 output_features.height = 1;
256}
257
258
259#define PCL_INSTANTIATE_MultiscaleFeaturePersistence(InT, Feature) template class PCL_EXPORTS pcl::MultiscaleFeaturePersistence<InT, Feature>;
260
261#endif /* PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_ */
DefaultPointRepresentation extends PointRepresentation to define default behavior for common point ty...
Generic class for extracting the persistent features from an input point cloud It can be given any Fe...
void determinePersistentFeatures(FeatureCloud &output_features, pcl::IndicesPtr &output_indices)
Central function that computes the persistent features.
void computeFeaturesAtAllScales()
Method that calls computeFeatureAtScale () for each scale parameter.
typename pcl::PointCloud< PointFeature >::Ptr FeatureCloudPtr
PCL base class.
Definition: pcl_base.h:70
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:147
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
reference emplace_back(Args &&...args)
Emplace a new point in the cloud, at the end of the container.
Definition: point_cloud.h:675
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:392
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:400
std::size_t size() const
Definition: point_cloud.h:443
float selectNorm(FloatVectorT a, FloatVectorT b, int dim, NormType norm_type)
Method that calculates any norm type available, based on the norm_type variable.
Definition: norms.hpp:50
@ L1
Definition: norms.h:54
shared_ptr< Indices > IndicesPtr
Definition: pcl_base.h:58