OutlierFiltersImpl.cpp

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// kate: replace-tabs off; indent-width 4; indent-mode normal
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/*
 
Copyright (c) 2010--2012,
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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      derived from this software without specific prior written permission.
 
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*/
 
#include "OutlierFiltersImpl.h"
#include "PointMatcherPrivate.h"
#include "Functions.h"
#include "MatchersImpl.h"
 
#include <algorithm>
#include <vector>
#include <iostream>
#include <limits>
#include <numeric>
#include <ciso646>
 
using namespace std;
using namespace PointMatcherSupport;
 
// NullOutlierFilter
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::NullOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        return OutlierWeights::Constant(input.ids.rows(), input.ids.cols(), 1);
}
 
template struct OutlierFiltersImpl<float>::NullOutlierFilter;
template struct OutlierFiltersImpl<double>::NullOutlierFilter;
 
 
// MaxDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::MaxDistOutlierFilter::MaxDistOutlierFilter(const Parameters& params):
        OutlierFilter("MaxDistOutlierFilter", MaxDistOutlierFilter::availableParameters(), params),
        maxDist(pow(Parametrizable::get<T>("maxDist"),2)) // we use the square distance later
{
}
 
 
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::MaxDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        return OutlierWeights((input.dists.array() <= maxDist).template cast<T>());
}
 
template struct OutlierFiltersImpl<float>::MaxDistOutlierFilter;
template struct OutlierFiltersImpl<double>::MaxDistOutlierFilter;
 
// MinDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::MinDistOutlierFilter::MinDistOutlierFilter(const Parameters& params):
        OutlierFilter("MinDistOutlierFilter", MinDistOutlierFilter::availableParameters(), params),
        minDist(pow(Parametrizable::get<T>("minDist"),2))// Note: we use the square distance later
{
}
 
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::MinDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        return OutlierWeights((input.dists.array() >= minDist).template cast<T>());
}
 
template struct OutlierFiltersImpl<float>::MinDistOutlierFilter;
template struct OutlierFiltersImpl<double>::MinDistOutlierFilter;
 
 
 
// MedianDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::MedianDistOutlierFilter::MedianDistOutlierFilter(const Parameters& params):
        OutlierFilter("MedianDistOutlierFilter", MedianDistOutlierFilter::availableParameters(), params),
        factor(Parametrizable::get<T>("factor"))
{
}
 
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::MedianDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        const T median = input.getDistsQuantile(0.5);
        const T limit = factor * median;
        return OutlierWeights((input.dists.array() <= limit).template cast<T>());
}
 
template struct OutlierFiltersImpl<float>::MedianDistOutlierFilter;
template struct OutlierFiltersImpl<double>::MedianDistOutlierFilter;
 
 
// TrimmedDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::TrimmedDistOutlierFilter::TrimmedDistOutlierFilter(const Parameters& params):
        OutlierFilter("TrimmedDistOutlierFilter", TrimmedDistOutlierFilter::availableParameters(), params),
        ratio(Parametrizable::get<T>("ratio"))
{
}
 
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::TrimmedDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        const T limit = input.getDistsQuantile(ratio);
        return OutlierWeights((input.dists.array() <= limit).template cast<T>());
}
 
template struct OutlierFiltersImpl<float>::TrimmedDistOutlierFilter;
template struct OutlierFiltersImpl<double>::TrimmedDistOutlierFilter;
 
// VarTrimmedDistOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::VarTrimmedDistOutlierFilter::VarTrimmedDistOutlierFilter(const Parameters& params):
        OutlierFilter("VarTrimmedDistOutlierFilter", VarTrimmedDistOutlierFilter::availableParameters(), params),
        minRatio(Parametrizable::get<T>("minRatio")),
        maxRatio(Parametrizable::get<T>("maxRatio")),
        lambda(Parametrizable::get<T>("lambda"))
{
        if (this->minRatio >= this->maxRatio)
        {
                throw InvalidParameter((boost::format("VarTrimmedDistOutlierFilter: minRatio (%1%) should be smaller than maxRatio (%2%)") % minRatio % maxRatio).str());
        }
}
 
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::VarTrimmedDistOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        const T tunedRatio = optimizeInlierRatio(input);
        LOG_INFO_STREAM("Optimized ratio: " << tunedRatio);
 
        const T limit = input.getDistsQuantile(tunedRatio);
        return OutlierWeights((input.dists.array() <= limit).template cast<T>());
}
 
template<typename T>
T OutlierFiltersImpl<T>::VarTrimmedDistOutlierFilter::optimizeInlierRatio(const Matches& matches)
{
        typedef typename PointMatcher<T>::ConvergenceError ConvergenceError;
        typedef typename Eigen::Array<T, Eigen::Dynamic, 1> LineArray;
 
        const int points_nbr = matches.dists.rows() * matches.dists.cols();
 
        // vector containing the squared distances of the matches
        std::vector<T> tmpSortedDist;
        tmpSortedDist.reserve(points_nbr);
        for (int x = 0; x < matches.dists.cols(); ++x)
                for (int y = 0; y < matches.dists.rows(); ++y)
                        if ((matches.dists(y, x) != numeric_limits<T>::infinity()) && (matches.dists(y, x) > 0))
                                tmpSortedDist.push_back(matches.dists(y, x));
        if (tmpSortedDist.empty())
                throw ConvergenceError("Inlier ratio optimization failed due to absence of matches");
 
        std::sort(tmpSortedDist.begin(), tmpSortedDist.end());
        std::vector<T> tmpCumSumSortedDist;
        tmpCumSumSortedDist.reserve(points_nbr);
        std::partial_sum(tmpSortedDist.begin(), tmpSortedDist.end(), tmpCumSumSortedDist.begin());
 
        const int minEl = floor(this->minRatio*points_nbr);
        const int maxEl = floor(this->maxRatio*points_nbr);
 
        // Return std::vector to an eigen::vector
        Eigen::Map<LineArray> sortedDist(&tmpCumSumSortedDist[0], points_nbr);
 
        const LineArray trunkSortedDist = sortedDist.segment(minEl, maxEl-minEl);
 
        const LineArray ids = LineArray::LinSpaced(trunkSortedDist.rows(), minEl+1, maxEl);
        const LineArray ratio = ids / points_nbr; // ratio for each of element between minEl and maxEl
        const LineArray deno = ratio.pow(this->lambda); // f^λ
        // frms = cumSumDists[minEl:maxEl] / id / (f^λ)²
        const LineArray FRMS = trunkSortedDist * ids.inverse() * deno.inverse().square() ;
        int minIndex(0);// = FRMS.minCoeff();
        FRMS.minCoeff(&minIndex);
        const T optRatio = (float)(minIndex + minEl)/ (float)points_nbr;
 
        return optRatio;
}
 
template struct OutlierFiltersImpl<float>::VarTrimmedDistOutlierFilter;
template struct OutlierFiltersImpl<double>::VarTrimmedDistOutlierFilter;
 
// SurfaceNormalOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::SurfaceNormalOutlierFilter::SurfaceNormalOutlierFilter(const Parameters& params):
        OutlierFilter("SurfaceNormalOutlierFilter", SurfaceNormalOutlierFilter::availableParameters(), params),
        eps(cos(Parametrizable::get<T>("maxAngle"))),
        warningPrinted(false)
{
        //warning: eps is change to cos(maxAngle)!
}
 
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::SurfaceNormalOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        const BOOST_AUTO(normalsReading, filteredReading.getDescriptorViewByName("normals"));
        const BOOST_AUTO(normalsReference, filteredReference.getDescriptorViewByName("normals"));
 
        // select weight from median
        OutlierWeights w(input.dists.rows(), input.dists.cols());
 
        if(normalsReading.cols() != 0 && normalsReference.cols() != 0)
        {
                for (int x = 0; x < w.cols(); ++x) // pts in reading
                {
                        const Vector normalRead = normalsReading.col(x).normalized();
 
                        for (int y = 0; y < w.rows(); ++y) // knn
                        {
                                const int idRef = input.ids(y, x);
 
                                if (idRef == MatchersImpl<T>::NNS::InvalidIndex) {
                                        w(y, x) = 0;
                                        continue;
                                }
 
                                const Vector normalRef = normalsReference.col(idRef).normalized();
 
                                const T value = normalRead.dot(normalRef);
 
                                if(value < eps) // test to keep the points
                                        w(y, x) = 0;
                                else
                                        w(y, x) = 1;
                        }
                }
        }
        else
        {
                if(warningPrinted == false)
                {
                        LOG_INFO_STREAM("SurfaceNormalOutlierFilter: surface normals not available. Skipping filtering");
                        warningPrinted = true;
                }
 
                w = Matrix::Ones(input.dists.rows(), input.dists.cols());
        }
        //abort();
        return w;
}
 
template struct OutlierFiltersImpl<float>::SurfaceNormalOutlierFilter;
template struct OutlierFiltersImpl<double>::SurfaceNormalOutlierFilter;
 
// GenericDescriptorOutlierFilter
template<typename T>
OutlierFiltersImpl<T>::GenericDescriptorOutlierFilter::GenericDescriptorOutlierFilter(const Parameters& params):
        OutlierFilter("GenericDescriptorOutlierFilter", GenericDescriptorOutlierFilter::availableParameters(), params),
        source(Parametrizable::getParamValueString("source")),
        descName(Parametrizable::getParamValueString("descName")),
        useSoftThreshold(Parametrizable::get<bool>("useSoftThreshold")),
        useLargerThan(Parametrizable::get<bool>("useLargerThan")),
        threshold(Parametrizable::get<T>("threshold"))
{
        if(source != "reference" && source != "reading")
        {
                throw InvalidParameter(
                (boost::format("GenericDescriptorOutlierFilter: Error, the parameter named 'source' can only be set to 'reference' or 'reading' but was set to %1%") % source).str());
        }
}
 
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::GenericDescriptorOutlierFilter::compute(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const Matches& input)
{
        typedef typename DataPoints::ConstView ConstView;
 
        const int knn = input.dists.rows();
        const int readPtsCount = input.dists.cols();
 
        OutlierWeights w(knn, readPtsCount);
 
        const DataPoints *cloud;
 
        if(source == "reference")
                cloud = &filteredReference;
        else
                cloud = &filteredReading;
 
        ConstView desc(cloud->getDescriptorViewByName(descName));
 
        if(desc.rows() != 1)
        {
                throw InvalidParameter(
                (boost::format("GenericDescriptorOutlierFilter: Error, the parameter named 'descName' must be a 1D descriptor but the field %1% is %2%D") % descName % desc.rows()).str());
        }
 
        for(int k=0; k < knn; k++)
        {
                for(int i=0; i < readPtsCount; i++)
                {
                        int point_id;
                        if (source == "reference") {
                                point_id = input.ids(k, i);
                                if (point_id == MatchersImpl<T>::NNS::InvalidIndex){
                                        LOG_INFO_STREAM("Invalid Index in GenericOutlierFilter, setting weight to 0.");
                                        w(k,i) = 0;
                                        continue;
                                }
                        } else {
                                // We don't need to look up corresponding points in the reference
                                //, we index into the reading PC directly.
                                point_id = i;
                        }
                        if(useSoftThreshold == false)
                        {
                                if(useLargerThan == true)
                                {
                                        if (desc(0, point_id) > threshold)
                                                w(k,i) = 1;
                                        else
                                                w(k,i) = 0;
                                }
                                else
                                {
                                        if (desc(0, point_id) < threshold)
                                                w(k,i) = 1;
                                        else
                                                w(k,i) = 0;
                                }
                        }
                        else
                        {
                                // use soft threshold by assigning the weight using the descriptor
                                w(k,i) = desc(0, point_id);
                        }
                }
        }
 
        //Normalize
        if(useSoftThreshold)
                w = w/w.maxCoeff();
 
        return w;
}
 
template struct OutlierFiltersImpl<float>::GenericDescriptorOutlierFilter;
template struct OutlierFiltersImpl<double>::GenericDescriptorOutlierFilter;
 
// RobustOutlierFilter
template<typename T>
typename OutlierFiltersImpl<T>::RobustOutlierFilter::RobustFctMap
OutlierFiltersImpl<T>::RobustOutlierFilter::robustFcts = {
        {"cauchy",  RobustFctId::Cauchy},
        {"welsch",  RobustFctId::Welsch},
        {"sc",      RobustFctId::SwitchableConstraint},
        {"gm",      RobustFctId::GM},
        {"tukey",   RobustFctId::Tukey},
        {"huber",   RobustFctId::Huber},
        {"L1",      RobustFctId::L1},
        {"student", RobustFctId::Student}
};
 
template<typename T>
OutlierFiltersImpl<T>::RobustOutlierFilter::RobustOutlierFilter(const std::string& className,
                const ParametersDoc paramsDoc,
                const Parameters& params):
        OutlierFilter(className, paramsDoc, params),
        robustFctName(Parametrizable::get<string>("robustFct")),
        tuning(Parametrizable::get<T>("tuning")),
        squaredApproximation(pow(Parametrizable::get<T>("approximation"), 2)),
        scaleEstimator(Parametrizable::get<string>("scaleEstimator")),
        nbIterationForScale(Parametrizable::get<int>("nbIterationForScale")),
        distanceType(Parametrizable::get<string>("distanceType")),
        robustFctId(-1),
        iteration(1),
        scale(0.0),
        berg_target_scale(0)
{
        const set<string> validScaleEstimator = {"none", "mad", "berg", "std"};
        if (validScaleEstimator.find(scaleEstimator) == validScaleEstimator.end()) {
                throw InvalidParameter("Invalid scale estimator name.");
        }
        const set<string> validDistanceType = {"point2point", "point2plane"};
        if (validDistanceType.find(distanceType) == validDistanceType.end()) {
                throw InvalidParameter("Invalid distance type name.");
        }
 
        resolveEstimatorName();
 
        if (scaleEstimator == "berg") {
                berg_target_scale = tuning;
 
                // See \cite{Bergstrom2014}
                if (robustFctId == RobustFctId::Cauchy)
                {
                        tuning = 4.3040;
                } else if (robustFctId == RobustFctId::Tukey)
                {
                        tuning = 7.0589;
                } else if (robustFctId == RobustFctId::Huber)
                {
                        tuning = 2.0138;
                }
        }
}
 
template<typename T>
OutlierFiltersImpl<T>::RobustOutlierFilter::RobustOutlierFilter(const Parameters& params):
        RobustOutlierFilter("RobustOutlierFilter", RobustOutlierFilter::availableParameters(), params)
{
}
 
 
template<typename T>
void OutlierFiltersImpl<T>::RobustOutlierFilter::resolveEstimatorName(){
        if (robustFcts.find(robustFctName) == robustFcts.end())
        {
                throw InvalidParameter("Invalid robust function name.");
        }
        robustFctId = robustFcts[robustFctName];
}
 
        template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::RobustOutlierFilter::compute(
                const DataPoints& filteredReading,
                const DataPoints& filteredReference,
                const Matches& input)
{
        return this->robustFiltering(filteredReading, filteredReference, input);
}
 
 
 
template<typename T>
typename PointMatcher<T>::Matrix
OutlierFiltersImpl<T>::RobustOutlierFilter::computePointToPlaneDistance(
                const DataPoints& reading,
                const DataPoints& reference,
                const Matches& input) {
 
        int nbr_read_point = input.dists.cols();
        int nbr_match = input.dists.rows();
 
        Matrix normals = reference.getDescriptorViewByName("normals");
 
        Vector reading_point(Vector::Zero(3));
        Vector reference_point(Vector::Zero(3));
        Vector normal(3);
 
        Matrix dists(Matrix::Zero(nbr_match, nbr_read_point));
 
        for(int i = 0; i < nbr_read_point; ++i)
        {
                reading_point = reading.features.block(0, i, 3, 1);
                for(int j = 0; j < nbr_match; ++j)
                {
                        const int reference_idx = input.ids(j, i);
                        if (reference_idx != Matches::InvalidId) {
                                reference_point = reference.features.block(0, reference_idx, 3, 1);
 
                                normal = normals.col(reference_idx).normalized();
                                // distance_point_to_plan = dot(n, p-q)²
                                dists(j, i) = pow(normal.dot(reading_point-reference_point), 2);
                        }
                }
        }
 
        return dists;
}
 
template<typename T>
typename PointMatcher<T>::OutlierWeights OutlierFiltersImpl<T>::RobustOutlierFilter::robustFiltering(
                const DataPoints& filteredReading,
                const DataPoints& filteredReference,
                const Matches& input) {
 
        if (scaleEstimator == "mad")
        {
                if (iteration <= nbIterationForScale or nbIterationForScale == 0)
                {
                        scale = sqrt(input.getMedianAbsDeviation());
                }
        } else if (scaleEstimator == "std")
        {
                if (iteration <= nbIterationForScale or nbIterationForScale == 0)
                {
                        scale = sqrt(input.getStandardDeviation());
                }
        } else if (scaleEstimator == "berg")
        {
                if (iteration <= nbIterationForScale or nbIterationForScale == 0)
                {
                        // The tuning constant is the target scale that we want to reach
                        // It's a bit confusing to use the tuning constant for scaling...
                        if (iteration == 1)
                        {
                                scale = 1.9 * sqrt(input.getDistsQuantile(0.5));
                        }
                        else
                        { // TODO: maybe add it has another parameter or make him a function of the max iteration
                                const T CONVERGENCE_RATE = 0.85;
                                scale = CONVERGENCE_RATE * (scale - berg_target_scale) + berg_target_scale;
                        }
                }
        }
        else
        {
                scale = 1.0; // We don't rescale
        }
        iteration++;
 
        Matrix dists = distanceType == "point2point" ? input.dists : computePointToPlaneDistance(filteredReading, filteredReference, input);
 
        // e² = scaled squared distance
        Array e2 = dists.array() / (scale * scale);
 
        T k = tuning;
        const T k2 = k * k;
        Array w, aboveThres, belowThres;
        switch (robustFctId) {
                case RobustFctId::Cauchy: // 1/(1 + e²/k²)
                        w = (1 + e2 / k2).inverse();
                        break;
                case RobustFctId::Welsch: // exp(-e²/k²)
                        w = (-e2 / k2).exp();
                        break;
                case RobustFctId::SwitchableConstraint: // if e² > k then 4 * k²/(k + e²)²
                        aboveThres = 4.0 * k2 * ((k + e2).square()).inverse();
                        w = (e2 >= k).select(aboveThres, 1.0);
                        break;
                case RobustFctId::GM:    // k²/(k + e²)²
                        w = k2*((k + e2).square()).inverse();
                        break;
                case RobustFctId::Tukey: // if e² < k² then (1-e²/k²)²
                        belowThres = (1 - e2 / k2).square();
                        w = (e2 >= k2).select(0.0, belowThres);
                        break;
                case RobustFctId::Huber: // if |e| >= k then k/|e| = k/sqrt(e²)
                        aboveThres = k * (e2.sqrt().inverse());
                        w = (e2 >= k2).select(aboveThres, 1.0);
                        break;
                case RobustFctId::L1: // 1/|e| = 1/sqrt(e²)
                        w = e2.sqrt().inverse();
                        break;
                case RobustFctId::Student: { // ....
                        const T d = 3;
                        auto p = (1 + e2 / k).pow(-(k + d) / 2);
                        w = p * (k + d) * (k + e2).inverse();
                        break;
                }
                default:
                        break;
        }
 
        // In the minimizer, zero weight are ignored, we want them to be notice by having the smallest value
        // The value can not be a numeric limit, since they might cause a nan/inf.
        const double ARBITRARY_SMALL_VALUE = 1e-50;
        w = (w.array() <= ARBITRARY_SMALL_VALUE).select(ARBITRARY_SMALL_VALUE, w);
 
 
        if(squaredApproximation != std::numeric_limits<T>::infinity())
        {
                //Note from Eigen documentation: (if statement).select(then matrix, else matrix)
                w = (e2 >= squaredApproximation).select(0.0, w);
        }
 
        return w;
}
 
 
template struct OutlierFiltersImpl<float>::RobustOutlierFilter;
template struct OutlierFiltersImpl<double>::RobustOutlierFilter;