Point Cloud Library (PCL) 1.12.0
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normal_refinement.hpp
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40
41#ifndef PCL_FILTERS_IMPL_NORMAL_REFINEMENT_H_
42#define PCL_FILTERS_IMPL_NORMAL_REFINEMENT_H_
43
44#include <pcl/filters/normal_refinement.h>
45
46///////////////////////////////////////////////////////////////////////////////////////////
47template <typename NormalT> void
49{
50 // Check input
51 if (input_->empty ())
52 {
53 PCL_ERROR ("[pcl::%s::applyFilter] No source was input!\n",
54 getClassName ().c_str ());
55 }
56
57 // Copy to output
58 output = *input_;
59
60 // Check that correspondences are non-empty
61 if (k_indices_.empty () || k_sqr_distances_.empty ())
62 {
63 PCL_ERROR ("[pcl::%s::applyFilter] No point correspondences given! Returning original input.\n",
64 getClassName ().c_str ());
65 return;
66 }
67
68 // Check that correspondences are OK
69 const unsigned int size = k_indices_.size ();
70 if (k_sqr_distances_.size () != size || input_->size () != size)
71 {
72 PCL_ERROR ("[pcl::%s::applyFilter] Inconsistency between size of correspondence indices/distances or input! Returning original input.\n",
73 getClassName ().c_str ());
74 return;
75 }
76
77 // Run refinement while monitoring convergence
78 for (unsigned int i = 0; i < max_iterations_; ++i)
79 {
80 // Output of the current iteration
81 PointCloud tmp = output;
82
83 // Mean change in direction, measured by dot products
84 float ddot = 0.0f;
85
86 // Loop over all points in current output and write refined normal to tmp
87 int num_valids = 0;
88 for(unsigned int j = 0; j < size; ++j)
89 {
90 // Point to write to
91 NormalT& tmpj = tmp[j];
92
93 // Refine
94 if (refineNormal (output, j, k_indices_[j], k_sqr_distances_[j], tmpj))
95 {
96 // Accumulate using similarity in direction between previous iteration and current
97 const NormalT& outputj = output[j];
98 ddot += tmpj.normal_x * outputj.normal_x + tmpj.normal_y * outputj.normal_y + tmpj.normal_z * outputj.normal_z;
99 ++num_valids;
100 }
101 }
102
103 // Take mean of similarities
104 ddot /= static_cast<float> (num_valids);
105
106 // Negate to since we want an error measure to approach zero
107 ddot = 1.0f - ddot;
108
109 // Update output
110 output = tmp;
111
112 // Break if converged
113 if (ddot < convergence_threshold_)
114 {
115 PCL_DEBUG("[pcl::%s::applyFilter] Converged after %i iterations with mean error of %f.\n",
116 getClassName ().c_str (), i+1, ddot);
117 break;
118 }
119 }
120}
121
122#endif
Iterator class for point clouds with or without given indices.
ConstCloudIterator(const PointCloud< PointT > &cloud)
std::size_t size() const
Size of the range the iterator is going through.
void applyFilter(PointCloud &output) override
Filter a Point Cloud.
bool refineNormal(const PointCloud< NormalT > &cloud, int index, const Indices &k_indices, const std::vector< float > &k_sqr_distances, NormalT &point)
Refine an indexed point based on its neighbors, this function only writes to the normal_* fields.
A point structure representing normal coordinates and the surface curvature estimate.