sac_model_sphere.h

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#pragma once
 
#ifdef __SSE__
#include <xmmintrin.h> // for __m128
#endif // ifdef __SSE__
#ifdef __AVX__
#include <immintrin.h> // for __m256
#endif // ifdef __AVX__
 
#include <pcl/sample_consensus/sac_model.h>
#include <pcl/sample_consensus/model_types.h>
 
namespace pcl
{
  /** \brief SampleConsensusModelSphere defines a model for 3D sphere segmentation.
    * The model coefficients are defined as:
    *   - \b center.x : the X coordinate of the sphere's center
    *   - \b center.y : the Y coordinate of the sphere's center
    *   - \b center.z : the Z coordinate of the sphere's center
    *   - \b radius   : the sphere's radius
    *
    * \author Radu B. Rusu
    * \ingroup sample_consensus
    */
  template <typename PointT>
  class SampleConsensusModelSphere : public SampleConsensusModel<PointT>
  {
    public:
      using SampleConsensusModel<PointT>::model_name_;
      using SampleConsensusModel<PointT>::input_;
      using SampleConsensusModel<PointT>::indices_;
      using SampleConsensusModel<PointT>::radius_min_;
      using SampleConsensusModel<PointT>::radius_max_;
      using SampleConsensusModel<PointT>::error_sqr_dists_;
 
      using PointCloud = typename SampleConsensusModel<PointT>::PointCloud;
      using PointCloudPtr = typename SampleConsensusModel<PointT>::PointCloudPtr;
      using PointCloudConstPtr = typename SampleConsensusModel<PointT>::PointCloudConstPtr;
 
      using Ptr = shared_ptr<SampleConsensusModelSphere<PointT> >;
      using ConstPtr = shared_ptr<const SampleConsensusModelSphere<PointT>>;
 
      /** \brief Constructor for base SampleConsensusModelSphere.
        * \param[in] cloud the input point cloud dataset
        * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
        */
      SampleConsensusModelSphere (const PointCloudConstPtr &cloud,
                                  bool random = false)
        : SampleConsensusModel<PointT> (cloud, random)
      {
        model_name_ = "SampleConsensusModelSphere";
        sample_size_ = 4;
        model_size_ = 4;
      }
 
      /** \brief Constructor for base SampleConsensusModelSphere.
        * \param[in] cloud the input point cloud dataset
        * \param[in] indices a vector of point indices to be used from \a cloud
        * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
        */
      SampleConsensusModelSphere (const PointCloudConstPtr &cloud,
                                  const Indices &indices,
                                  bool random = false)
        : SampleConsensusModel<PointT> (cloud, indices, random)
      {
        model_name_ = "SampleConsensusModelSphere";
        sample_size_ = 4;
        model_size_ = 4;
      }
      
      /** \brief Empty destructor */
      ~SampleConsensusModelSphere () override = default;
 
      /** \brief Copy constructor.
        * \param[in] source the model to copy into this
        */
      SampleConsensusModelSphere (const SampleConsensusModelSphere &source) :
        SampleConsensusModel<PointT> ()
      {
        *this = source;
        model_name_ = "SampleConsensusModelSphere";
      }
 
      /** \brief Copy constructor.
        * \param[in] source the model to copy into this
        */
      inline SampleConsensusModelSphere&
      operator = (const SampleConsensusModelSphere &source)
      {
        SampleConsensusModel<PointT>::operator=(source);
        return (*this);
      }
 
      /** \brief Check whether the given index samples can form a valid sphere model, compute the model 
        * coefficients from these samples and store them internally in model_coefficients. 
        * The sphere coefficients are: x, y, z, R.
        * \param[in] samples the point indices found as possible good candidates for creating a valid model
        * \param[out] model_coefficients the resultant model coefficients
        */
      bool
      computeModelCoefficients (const Indices &samples,
                                Eigen::VectorXf &model_coefficients) const override;
 
      /** \brief Compute all distances from the cloud data to a given sphere model.
        * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
        * \param[out] distances the resultant estimated distances
        */
      void
      getDistancesToModel (const Eigen::VectorXf &model_coefficients,
                           std::vector<double> &distances) const override;
 
      /** \brief Select all the points which respect the given model coefficients as inliers.
        * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
        * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
        * \param[out] inliers the resultant model inliers
        */
      void 
      selectWithinDistance (const Eigen::VectorXf &model_coefficients, 
                            const double threshold, 
                            Indices &inliers) override;
 
      /** \brief Count all the points which respect the given model coefficients as inliers. 
        * 
        * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
        * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
        * \return the resultant number of inliers
        */
      std::size_t
      countWithinDistance (const Eigen::VectorXf &model_coefficients,
                           const double threshold) const override;
 
      /** \brief Recompute the sphere coefficients using the given inlier set and return them to the user.
        * @note: these are the coefficients of the sphere model after refinement (e.g. after SVD)
        * \param[in] inliers the data inliers found as supporting the model
        * \param[in] model_coefficients the initial guess for the optimization
        * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
        */
      void
      optimizeModelCoefficients (const Indices &inliers,
                                 const Eigen::VectorXf &model_coefficients,
                                 Eigen::VectorXf &optimized_coefficients) const override;
 
      /** \brief Create a new point cloud with inliers projected onto the sphere model.
        * \param[in] inliers the data inliers that we want to project on the sphere model
        * \param[in] model_coefficients the coefficients of a sphere model
        * \param[out] projected_points the resultant projected points
        * \param[in] copy_data_fields set to true if we need to copy the other data fields
        * \todo implement this.
        */
      void
      projectPoints (const Indices &inliers,
                     const Eigen::VectorXf &model_coefficients,
                     PointCloud &projected_points,
                     bool copy_data_fields = true) const override;
 
      /** \brief Verify whether a subset of indices verifies the given sphere model coefficients.
        * \param[in] indices the data indices that need to be tested against the sphere model
        * \param[in] model_coefficients the sphere model coefficients
        * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
        */
      bool
      doSamplesVerifyModel (const std::set<index_t> &indices,
                            const Eigen::VectorXf &model_coefficients,
                            const double threshold) const override;
 
      /** \brief Return a unique id for this model (SACMODEL_SPHERE). */
      inline pcl::SacModel getModelType () const override { return (SACMODEL_SPHERE); }
 
    protected:
      using SampleConsensusModel<PointT>::sample_size_;
      using SampleConsensusModel<PointT>::model_size_;
 
      /** \brief Check whether a model is valid given the user constraints.
        * \param[in] model_coefficients the set of model coefficients
        */
      bool
      isModelValid (const Eigen::VectorXf &model_coefficients) const override
      {
        if (!SampleConsensusModel<PointT>::isModelValid (model_coefficients))
          return (false);
 
        if (radius_min_ != -std::numeric_limits<double>::max() && model_coefficients[3] < radius_min_)
          return (false);
        if (radius_max_ != std::numeric_limits<double>::max() && model_coefficients[3] > radius_max_)
          return (false);
 
        return (true);
      }
 
      /** \brief Check if a sample of indices results in a good sample of points
        * indices.
        * \param[in] samples the resultant index samples
        */
      bool
      isSampleGood(const Indices &samples) const override;
 
      /** This implementation uses no SIMD instructions. It is not intended for normal use.
        * See countWithinDistance which automatically uses the fastest implementation.
        */
      std::size_t
      countWithinDistanceStandard (const Eigen::VectorXf &model_coefficients,
                                   const double threshold,
                                   std::size_t i = 0) const;
 
#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
      /** This implementation uses SSE, SSE2, and SSE4.1 instructions. It is not intended for normal use.
        * See countWithinDistance which automatically uses the fastest implementation.
        */
      std::size_t
      countWithinDistanceSSE (const Eigen::VectorXf &model_coefficients,
                              const double threshold,
                              std::size_t i = 0) const;
#endif
 
#if defined (__AVX__) && defined (__AVX2__)
      /** This implementation uses AVX and AVX2 instructions. It is not intended for normal use.
        * See countWithinDistance which automatically uses the fastest implementation.
        */
      std::size_t
      countWithinDistanceAVX (const Eigen::VectorXf &model_coefficients,
                              const double threshold,
                              std::size_t i = 0) const;
#endif
 
    private:
      struct OptimizationFunctor : pcl::Functor<float>
      {
        /** Functor constructor
          * \param[in] indices the indices of data points to evaluate
          * \param[in] estimator pointer to the estimator object
          */
        OptimizationFunctor (const pcl::SampleConsensusModelSphere<PointT> *model, const Indices& indices) :
          pcl::Functor<float> (indices.size ()), model_ (model), indices_ (indices) {}
 
        /** Cost function to be minimized
          * \param[in] x the variables array
          * \param[out] fvec the resultant functions evaluations
          * \return 0
          */
        int 
        operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
        {
          Eigen::Vector4f cen_t;
          cen_t[3] = 0;
          for (int i = 0; i < values (); ++i)
          {
            // Compute the difference between the center of the sphere and the datapoint X_i
            cen_t.head<3>() = (*model_->input_)[indices_[i]].getVector3fMap() - x.head<3>();
 
            // g = sqrt ((x-a)^2 + (y-b)^2 + (z-c)^2) - R
            fvec[i] = std::sqrt (cen_t.dot (cen_t)) - x[3];
          }
          return (0);
        }
 
        const pcl::SampleConsensusModelSphere<PointT> *model_;
        const Indices &indices_;
      };
 
#ifdef __AVX__
      inline __m256 sqr_dist8 (const std::size_t i, const __m256 a_vec, const __m256 b_vec, const __m256 c_vec) const;
#endif
 
#ifdef __SSE__
      inline __m128 sqr_dist4 (const std::size_t i, const __m128 a_vec, const __m128 b_vec, const __m128 c_vec) const;
#endif
   };
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/sample_consensus/impl/sac_model_sphere.hpp>
#endif