SpectralDecomposition.h

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// kate: replace-tabs off; indent-width 4; indent-mode normal
// vim: ts=4:sw=4:noexpandtab
/*
 
Copyright (c) 2010--2018,
François Pomerleau and Stephane Magnenat, ASL, ETHZ, Switzerland
You can contact the authors at <f dot pomerleau at gmail dot com> and
<stephane at magnenat dot net>
 
All rights reserved.
 
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modification, are permitted provided that the following conditions are met:
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      documentation and/or other materials provided with the distribution.
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      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.
 
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*/
#pragma once
 
#include "PointMatcher.h"
#include "utils/sparsetv.h"
 
template<typename T>
struct SpectralDecompositionDataPointsFilter : public PointMatcher<T>::DataPointsFilter
{
        // Type definitions
        typedef PointMatcher<T> PM;
        typedef typename PM::DataPoints DataPoints;
        typedef typename PM::DataPoints DP;
        typedef typename PM::DataPointsFilter DataPointsFilter;
 
        typedef PointMatcherSupport::Parametrizable Parametrizable;
        typedef PointMatcherSupport::Parametrizable P;
        typedef Parametrizable::Parameters Parameters;
        typedef Parametrizable::ParameterDoc ParameterDoc;
        typedef Parametrizable::ParametersDoc ParametersDoc;
        typedef Parametrizable::InvalidParameter InvalidParameter;
        
        typedef typename DataPoints::Index Index;
 
        typedef typename PointMatcher<T>::DataPoints::InvalidField InvalidField;
        
        typedef typename PM::Matrix Matrix;
        typedef typename PM::Vector Vector;
 
        inline static const std::string description()
        {
                return "Point cloud sampling and enhancement: compute geometric features saliencies throught Tensor Voting framework and use them to sample the point cloud. \\cite{Labussiere2020}";
        }
 
        inline static const ParametersDoc availableParameters()
        {
                return {
                        {"k", "Number of neighbors to consider", "50", "6", "4294967295", &P::Comp<std::size_t>},
                        {"sigma", "Scale of the vote in TensorVoting.", "0.2", "0.", "+inf", &P::Comp<T>},
                        {"radius", "Radius to control the scale of the uniform distribution.", "0.4", "0.", "+inf", &P::Comp<T>},
                        {"itMax", "Number max of iterations to do", "10", "1", "4294967295", &P::Comp<std::size_t>},
                        {"keepNormals", "Flag to keep normals computed by TV.", "1", "0", "1", P::Comp<bool>},
                        {"keepLabels", "Flag to keep labels computed by TV.", "1", "0", "1", P::Comp<bool>},
                        {"keepLambdas", "Flag to keep lambdas computed by TV.", "1", "0", "1", P::Comp<bool>},
                        {"keepTensors", "Flag to keep elements Tensors computed by TV.", "1", "0", "1", P::Comp<bool>}
                };
        }
 
public:
        const std::size_t k;
        const T sigma;
        const T radius;
        const std::size_t itMax;
        const bool keepNormals;
        const bool keepLabels;
        const bool keepLambdas;
        const bool keepTensors;
        
        //Ctor, uses parameter interface
        SpectralDecompositionDataPointsFilter(const Parameters& params = Parameters());
        //SpectralDecompositionDataPointsFilter();
        
        //Dtor
        virtual ~SpectralDecompositionDataPointsFilter() {};
 
        virtual DataPoints filter(const DataPoints& input);
        virtual void inPlaceFilter(DataPoints& cloud);
 
private:
        static T xi_expectation(const std::size_t D, const T sigma_, const T radius_)
        {
                return (D == 1) ? //on a curve
                        (std::sqrt(M_PI * sigma_) *  std::erf(radius_ / std::sqrt(sigma_))) / (2. * radius_)
                : (D == 2) ? //on a surface
                        (sigma_ - sigma_ * std::exp(- radius_ * radius_ / sigma_)) / (radius_ * radius_)
                : (D == 3) ?//on a sphere
                        3. * sigma_ * (std::sqrt(M_PI * sigma_) *  std::erf(radius_ / std::sqrt(sigma_)) - 2. * radius_ * std::exp(- radius_ * radius_ / sigma_)) / (4. * radius_ * radius_ * radius_)
                : T(1.); //otherwise
        }
        
        void addDescriptor(DataPoints& pts, const TensorVoting<T> &tv, bool keepNormals_, bool keepLabels_, bool keepLambdas_, bool keepTensors_) const;
        
        void removeOutlier(DataPoints& pts, const TensorVoting<T> &tv) const;
        
        void filterSurfaceness(DataPoints& pts, T xi, std::size_t k) const;
        void filterCurveness(DataPoints& pts, T xi, std::size_t k) const;
        void filterPointness(DataPoints& pts, T xi, std::size_t k) const;
};