rift.hpp

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#ifndef PCL_FEATURES_IMPL_RIFT_H_
#define PCL_FEATURES_IMPL_RIFT_H_
 
#include <pcl/features/rift.h>
 
#include <Eigen/Core>           // for Eigen::Map<const Eigen::Vector3f>
 
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename GradientT, typename PointOutT> void
pcl::RIFTEstimation<PointInT, GradientT, PointOutT>::computeRIFT (
      const PointCloudIn &cloud, const PointCloudGradient &gradient, 
      int p_idx, float radius, const pcl::Indices &indices, 
      const std::vector<float> &sqr_distances, Eigen::MatrixXf &rift_descriptor)
{
  if (indices.empty ())
  {
    PCL_ERROR ("[pcl::RIFTEstimation] Null indices points passed!\n");
    return;
  }
 
  // Determine the number of bins to use based on the size of rift_descriptor
  int nr_distance_bins = static_cast<int> (rift_descriptor.rows ());
  int nr_gradient_bins = static_cast<int> (rift_descriptor.cols ());
 
  // Get the center point
  const Eigen::Map<const Eigen::Vector3f> p0 = cloud[p_idx].getVector3fMap ();
 
  // Compute the RIFT descriptor
  rift_descriptor.setZero ();
  for (std::size_t idx = 0; idx < indices.size (); ++idx)
  {
    // Compute the gradient magnitude and orientation (relative to the center point)
    const Eigen::Map<const Eigen::Vector3f> point = cloud[indices[idx]].getVector3fMap ();
    Eigen::Map<const Eigen::Vector3f> gradient_vector (& (gradient[indices[idx]].gradient[0]));
 
    float gradient_magnitude = gradient_vector.norm ();
    float gradient_angle_from_center = std::acos (gradient_vector.dot ((point - p0).normalized ()) / gradient_magnitude);
    if (!std::isfinite (gradient_angle_from_center))
      gradient_angle_from_center = 0.0;
 
    // Normalize distance and angle values to: 0.0 <= d,g < nr_distances_bins,nr_gradient_bins
    const float eps = std::numeric_limits<float>::epsilon ();
    float d = static_cast<float> (nr_distance_bins) * std::sqrt (sqr_distances[idx]) / (radius + eps);
    float g = static_cast<float> (nr_gradient_bins) * gradient_angle_from_center / (static_cast<float> (M_PI) + eps);
 
    // Compute the bin indices that need to be updated
    int d_idx_min = (std::max)(static_cast<int> (std::ceil (d - 1)), 0);
    int d_idx_max = (std::min)(static_cast<int> (std::floor (d + 1)), nr_distance_bins - 1);
    int g_idx_min = static_cast<int> (std::ceil (g - 1));
    int g_idx_max = static_cast<int> (std::floor (g + 1));
 
    // Update the appropriate bins of the histogram 
    for (int g_idx = g_idx_min; g_idx <= g_idx_max; ++g_idx)  
    {
      // Because gradient orientation is cyclical, out-of-bounds values must wrap around
      int g_idx_wrapped = ((g_idx + nr_gradient_bins) % nr_gradient_bins); 
 
      for (int d_idx = d_idx_min; d_idx <= d_idx_max; ++d_idx)
      {
        // To avoid boundary effects, use linear interpolation when updating each bin 
        float w = (1.0f - std::abs (d - static_cast<float> (d_idx))) * (1.0f - std::abs (g - static_cast<float> (g_idx)));
 
        rift_descriptor (d_idx, g_idx_wrapped) += w * gradient_magnitude;
      }
    }
  }
 
  // Normalize the RIFT descriptor to unit magnitude
  rift_descriptor.normalize ();
}
 
 
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename GradientT, typename PointOutT> void
pcl::RIFTEstimation<PointInT, GradientT, PointOutT>::computeFeature (PointCloudOut &output)
{
  // Make sure a search radius is set
  if (search_radius_ == 0.0)
  {
    PCL_ERROR ("[pcl::%s::computeFeature] The search radius must be set before computing the feature!\n",
               getClassName ().c_str ());
    output.width = output.height = 0;
    output.clear ();
    return;
  }
 
  // Make sure the RIFT descriptor has valid dimensions
  if (nr_gradient_bins_ <= 0)
  {
    PCL_ERROR ("[pcl::%s::computeFeature] The number of gradient bins must be greater than zero!\n",
               getClassName ().c_str ());
    output.width = output.height = 0;
    output.clear ();
    return;
  }
  if (nr_distance_bins_ <= 0)
  {
    PCL_ERROR ("[pcl::%s::computeFeature] The number of distance bins must be greater than zero!\n",
               getClassName ().c_str ());
    output.width = output.height = 0;
    output.clear ();
    return;
  }
 
  // Check for valid input gradient
  if (!gradient_)
  {
    PCL_ERROR ("[pcl::%s::computeFeature] No input gradient was given!\n", getClassName ().c_str ());
    output.width = output.height = 0;
    output.clear ();
    return;
  }
  if (gradient_->size () != surface_->size ())
  {
    PCL_ERROR ("[pcl::%s::computeFeature] ", getClassName ().c_str ());
    PCL_ERROR ("The number of points in the input dataset differs from the number of points in the gradient!\n");
    output.width = output.height = 0;
    output.clear ();
    return;
  }
 
  Eigen::MatrixXf rift_descriptor (nr_distance_bins_, nr_gradient_bins_);
  pcl::Indices nn_indices;
  std::vector<float> nn_dist_sqr;
 
  // Iterating over the entire index vector
  for (std::size_t idx = 0; idx < indices_->size (); ++idx)
  {
    // Find neighbors within the search radius
    tree_->radiusSearch ((*indices_)[idx], search_radius_, nn_indices, nn_dist_sqr);
 
    // Compute the RIFT descriptor
    computeRIFT (*surface_, *gradient_, (*indices_)[idx], static_cast<float> (search_radius_), nn_indices, nn_dist_sqr, rift_descriptor);
 
    // Default layout is column major, copy elementwise
    std::copy (rift_descriptor.data (), rift_descriptor.data () + rift_descriptor.size (), output[idx].histogram);
  }
}
 
#define PCL_INSTANTIATE_RIFTEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::RIFTEstimation<T,NT,OutT>;
 
#endif    // PCL_FEATURES_IMPL_RIFT_H_