normal_refinement.hpp
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00040 
00041 #ifndef PCL_FILTERS_IMPL_NORMAL_REFINEMENT_H_
00042 #define PCL_FILTERS_IMPL_NORMAL_REFINEMENT_H_
00043 
00044 #include <pcl/filters/normal_refinement.h>
00045 
00047 template <typename NormalT> void
00048 pcl::NormalRefinement<NormalT>::applyFilter (PointCloud &output)
00049 {
00050   // Check input
00051   if (input_->empty ())
00052   {
00053     PCL_ERROR ("[pcl::%s::applyFilter] No source was input!\n",
00054                getClassName ().c_str ());
00055   }
00056   
00057   // Copy to output
00058   output = *input_;
00059   
00060   // Check that correspondences are non-empty
00061   if (k_indices_.empty () || k_sqr_distances_.empty ())
00062   {
00063     PCL_ERROR ("[pcl::%s::applyFilter] No point correspondences given! Returning original input.\n",
00064                getClassName ().c_str ());
00065     return;
00066   }
00067 
00068   // Check that correspondences are OK
00069   const unsigned int size = k_indices_.size ();
00070   if (k_sqr_distances_.size () != size || input_->size () != size)
00071   {
00072     PCL_ERROR ("[pcl::%s::applyFilter] Inconsistency between size of correspondence indices/distances or input! Returning original input.\n",
00073                getClassName ().c_str ());
00074     return;
00075   }
00076   
00077   // Run refinement while monitoring convergence
00078   for (unsigned int i = 0; i < max_iterations_; ++i)
00079   {
00080     // Output of the current iteration
00081     PointCloud tmp = output;
00082     
00083     // Mean change in direction, measured by dot products
00084     float ddot = 0.0f;
00085     
00086     // Loop over all points in current output and write refined normal to tmp
00087     int num_valids = 0;
00088     for(unsigned int j = 0; j < size; ++j)
00089     {
00090       // Point to write to
00091       NormalT& tmpj = tmp[j];
00092       
00093       // Refine
00094       if (refineNormal (output, j, k_indices_[j], k_sqr_distances_[j], tmpj))
00095       {
00096         // Accumulate using similarity in direction between previous iteration and current
00097         const NormalT& outputj = output[j];
00098         ddot += tmpj.normal_x * outputj.normal_x + tmpj.normal_y * outputj.normal_y + tmpj.normal_z * outputj.normal_z;
00099         ++num_valids;
00100       }
00101     }
00102     
00103     // Take mean of similarities
00104     ddot /= static_cast<float> (num_valids);
00105     
00106     // Negate to since we want an error measure to approach zero
00107     ddot = 1.0f - ddot;
00108     
00109     // Update output
00110     output = tmp;
00111     
00112     // Break if converged
00113     if (ddot < convergence_threshold_)
00114     {
00115       PCL_DEBUG("[pcl::%s::applyFilter] Converged after %i iterations with mean error of %f.\n",
00116                 getClassName ().c_str (), i+1, ddot);
00117       break;
00118     }
00119   }
00120 }
00121 
00122 #endif


pcl
Author(s): Open Perception
autogenerated on Wed Aug 26 2015 15:25:58