Point Cloud Library (PCL) 1.15.0
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correspondence_estimation_backprojection.hpp
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39
40#ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_
41#define PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_
42
43#include <pcl/common/copy_point.h>
44
45namespace pcl {
46
47namespace registration {
48
49template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
50bool
53{
54 if (!source_normals_ || !target_normals_) {
55 PCL_WARN("[pcl::registration::%s::initCompute] Datasets containing normals for "
56 "source/target have not been given!\n",
57 getClassName().c_str());
58 return (false);
59 }
60
61 return (
63}
64
65///////////////////////////////////////////////////////////////////////////////////////////
66template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
67void
69 determineCorrespondences(pcl::Correspondences& correspondences, double max_distance)
70{
71 if (!initCompute())
72 return;
73
74 correspondences.resize(indices_->size());
75
76 pcl::Indices nn_indices(k_);
77 std::vector<float> nn_dists(k_);
78
79 int min_index = 0;
80
82 unsigned int nr_valid_correspondences = 0;
83
84 // Iterate over the input set of source indices
85 for (const auto& idx_i : (*indices_)) {
86 const auto& pt = detail::pointCopyOrRef<PointTarget, PointSource>(input_, idx_i);
87 tree_->nearestKSearch(pt, k_, nn_indices, nn_dists);
88
89 // Among the K nearest neighbours find the one with minimum perpendicular distance
90 // to the normal
91 float min_dist = std::numeric_limits<float>::max();
92
93 // Find the best correspondence
94 for (std::size_t j = 0; j < nn_indices.size(); j++) {
95 float cos_angle = (*source_normals_)[idx_i].normal_x *
96 (*target_normals_)[nn_indices[j]].normal_x +
97 (*source_normals_)[idx_i].normal_y *
98 (*target_normals_)[nn_indices[j]].normal_y +
99 (*source_normals_)[idx_i].normal_z *
100 (*target_normals_)[nn_indices[j]].normal_z;
101 float dist = nn_dists[j] * (2.0f - cos_angle * cos_angle);
102
103 if (dist < min_dist) {
104 min_dist = dist;
105 min_index = static_cast<int>(j);
106 }
107 }
108 if (min_dist > max_distance)
109 continue;
110
111 corr.index_query = idx_i;
112 corr.index_match = nn_indices[min_index];
113 corr.distance = nn_dists[min_index]; // min_dist;
114 correspondences[nr_valid_correspondences++] = corr;
115 }
116 correspondences.resize(nr_valid_correspondences);
117 deinitCompute();
118}
119
120template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
121void
124 double max_distance)
125{
126 if (!initCompute())
127 return;
128
129 // Set the internal point representation of choice
130 if (!initComputeReciprocal())
131 return;
132
133 correspondences.resize(indices_->size());
134
135 pcl::Indices nn_indices(k_);
136 std::vector<float> nn_dists(k_);
137 pcl::Indices index_reciprocal(1);
138 std::vector<float> distance_reciprocal(1);
139
140 int min_index = 0;
141
143 unsigned int nr_valid_correspondences = 0;
144 int target_idx = 0;
145
146 // Iterate over the input set of source indices
147 for (const auto& idx_i : (*indices_)) {
148 // Check if the template types are the same. If true, avoid a copy.
149 // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
150 // macro!
151 tree_->nearestKSearch(
152 detail::pointCopyOrRef<PointTarget, PointSource>(input_, idx_i),
153 k_,
154 nn_indices,
155 nn_dists);
156
157 // Among the K nearest neighbours find the one with minimum perpendicular distance
158 // to the normal
159 float min_dist = std::numeric_limits<float>::max();
160
161 // Find the best correspondence
162 for (std::size_t j = 0; j < nn_indices.size(); j++) {
163 float cos_angle = (*source_normals_)[idx_i].normal_x *
164 (*target_normals_)[nn_indices[j]].normal_x +
165 (*source_normals_)[idx_i].normal_y *
166 (*target_normals_)[nn_indices[j]].normal_y +
167 (*source_normals_)[idx_i].normal_z *
168 (*target_normals_)[nn_indices[j]].normal_z;
169 float dist = nn_dists[j] * (2.0f - cos_angle * cos_angle);
170
171 if (dist < min_dist) {
172 min_dist = dist;
173 min_index = static_cast<int>(j);
174 }
175 }
176 if (min_dist > max_distance)
177 continue;
178
179 // Check if the correspondence is reciprocal
180 target_idx = nn_indices[min_index];
181 tree_reciprocal_->nearestKSearch(
182 detail::pointCopyOrRef<PointSource, PointTarget>(target_, target_idx),
183 1,
184 index_reciprocal,
185 distance_reciprocal);
186
187 if (idx_i != index_reciprocal[0])
188 continue;
189
190 corr.index_query = idx_i;
191 corr.index_match = nn_indices[min_index];
192 corr.distance = nn_dists[min_index]; // min_dist;
193 correspondences[nr_valid_correspondences++] = corr;
194 }
195 correspondences.resize(nr_valid_correspondences);
196 deinitCompute();
197}
198
199} // namespace registration
200} // namespace pcl
201
202#endif // PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_
virtual void determineReciprocalCorrespondences(pcl::Correspondences &correspondences, double max_distance=std::numeric_limits< double >::max())
Determine the reciprocal correspondences between input and target cloud.
void determineCorrespondences(pcl::Correspondences &correspondences, double max_distance=std::numeric_limits< double >::max())
Determine the correspondences between input and target cloud.
Abstract CorrespondenceEstimationBase class.
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
Correspondence represents a match between two entities (e.g., points, descriptors,...
index_t index_query
Index of the query (source) point.
index_t index_match
Index of the matching (target) point.