linear_least_squares_normal.h

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#pragma once
 
#include <pcl/features/feature.h>
 
namespace pcl
{
  /** \brief Surface normal estimation on dense data using a least-squares estimation based on a first-order Taylor approximation.
    * \author Stefan Holzer, Cedric Cagniart
    */
  template <typename PointInT, typename PointOutT>
  class LinearLeastSquaresNormalEstimation : public Feature<PointInT, PointOutT>
  {
    public:
      using Ptr = shared_ptr<LinearLeastSquaresNormalEstimation<PointInT, PointOutT> >;
      using ConstPtr = shared_ptr<const LinearLeastSquaresNormalEstimation<PointInT, PointOutT> >;
      using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
      using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
      using Feature<PointInT, PointOutT>::input_;
      using Feature<PointInT, PointOutT>::feature_name_;
      using Feature<PointInT, PointOutT>::tree_;
      using Feature<PointInT, PointOutT>::k_;
 
      /** \brief Constructor */
      LinearLeastSquaresNormalEstimation () :
          use_depth_dependent_smoothing_(false),
          max_depth_change_factor_(1.0f),
          normal_smoothing_size_(9.0f)
      {
          feature_name_ = "LinearLeastSquaresNormalEstimation";
          tree_.reset ();
          k_ = 1;
      };
 
      /** \brief Destructor */
      ~LinearLeastSquaresNormalEstimation ();
 
      /** \brief Computes the normal at the specified position. 
        * \param[in] pos_x x position (pixel)
        * \param[in] pos_y y position (pixel)
        * \param[out] normal the output estimated normal 
        */
      void
      computePointNormal (const int pos_x, const int pos_y, PointOutT &normal);
 
      /** \brief Set the normal smoothing size
        * \param[in] normal_smoothing_size factor which influences the size of the area used to smooth normals 
        * (depth dependent if useDepthDependentSmoothing is true)
        */
      void
      setNormalSmoothingSize (float normal_smoothing_size)
      {
        normal_smoothing_size_ = normal_smoothing_size;
      }
 
      /** \brief Set whether to use depth depending smoothing or not
        * \param[in] use_depth_dependent_smoothing decides whether the smoothing is depth dependent
        */
      void
      setDepthDependentSmoothing (bool use_depth_dependent_smoothing)
      {
        use_depth_dependent_smoothing_ = use_depth_dependent_smoothing;
      }
 
      /** \brief The depth change threshold for computing object borders
        * \param[in] max_depth_change_factor the depth change threshold for computing object borders based on 
        * depth changes
        */
      void 
      setMaxDepthChangeFactor (float max_depth_change_factor)
      {
        max_depth_change_factor_ = max_depth_change_factor;
      }
 
      /** \brief Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
        * \param[in] cloud the const boost shared pointer to a PointCloud message
        */
      inline void 
      setInputCloud (const typename PointCloudIn::ConstPtr &cloud) override 
      { 
        input_ = cloud; 
      }
 
    protected:
      /** \brief Computes the normal for the complete cloud. 
        * \param[out] output the resultant normals
        */
      void 
      computeFeature (PointCloudOut &output) override;
 
    private:
 
      /** the threshold used to detect depth discontinuities */
      //float distance_threshold_;
 
      /** \brief Smooth data based on depth (true/false). */
      bool use_depth_dependent_smoothing_;
 
      /** \brief Threshold for detecting depth discontinuities */
      float max_depth_change_factor_;
 
      /** \brief */
      float normal_smoothing_size_;
  };
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/features/impl/linear_least_squares_normal.hpp>
#endif