SamplingSurfaceNormal.cpp

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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.
 
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
    * Redistributions of source code must retain the above copyright
      notice, this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of the <organization> nor the
      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.
 
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY
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*/
#include "SamplingSurfaceNormal.h"
#include "DataPointsFilters/utils/utils.h"
 
// Eigenvalues
#include "Eigen/QR"
#include "Eigen/Eigenvalues"
 
#include "PointMatcherPrivate.h"
 
#include <utility>
#include <algorithm>
 
// SamplingSurfaceNormalDataPointsFilter
 
// Constructor
template<typename T>
SamplingSurfaceNormalDataPointsFilter<T>::SamplingSurfaceNormalDataPointsFilter(
        const Parameters& params):
        PointMatcher<T>::DataPointsFilter("SamplingSurfaceNormalDataPointsFilter",
                SamplingSurfaceNormalDataPointsFilter::availableParameters(), params),
        ratio(Parametrizable::get<T>("ratio")),
        knn(Parametrizable::get<int>("knn")),
        samplingMethod(Parametrizable::get<int>("samplingMethod")),
        maxBoxDim(Parametrizable::get<T>("maxBoxDim")),
        averageExistingDescriptors(Parametrizable::get<bool>("averageExistingDescriptors")),
        keepNormals(Parametrizable::get<bool>("keepNormals")),
        keepDensities(Parametrizable::get<bool>("keepDensities")),
        keepEigenValues(Parametrizable::get<bool>("keepEigenValues")),
        keepEigenVectors(Parametrizable::get<bool>("keepEigenVectors"))
{
}
 
// Compute
template<typename T>
typename PointMatcher<T>::DataPoints 
SamplingSurfaceNormalDataPointsFilter<T>::filter(
        const DataPoints& input)
{
        DataPoints output(input);
        inPlaceFilter(output);
        return output;
}
 
// In-place filter
template<typename T>
void SamplingSurfaceNormalDataPointsFilter<T>::inPlaceFilter(
        DataPoints& cloud)
{
        typedef typename DataPoints::View View;
        typedef typename DataPoints::Label Label;
        typedef typename DataPoints::Labels Labels;
 
        const int pointsCount(cloud.features.cols());
        const int featDim(cloud.features.rows());
        const int descDim(cloud.descriptors.rows());
        const unsigned int labelDim(cloud.descriptorLabels.size());
 
        int insertDim(0);
        if (averageExistingDescriptors)
        {
                // TODO: this should be in the form of an assert
                // Validate descriptors and labels
                for(unsigned int i = 0; i < labelDim ; ++i)
                        insertDim += cloud.descriptorLabels[i].span;
                if (insertDim != descDim)
                        throw InvalidField("SamplingSurfaceNormalDataPointsFilter: Error, descriptor labels do not match descriptor data");
        }
 
        // Compute space requirement for new descriptors
        const int dimNormals(featDim-1);
        const int dimDensities(1);
        const int dimEigValues(featDim-1);
        const int dimEigVectors((featDim-1)*(featDim-1));
 
        // Allocate space for new descriptors
        Labels cloudLabels;
        if (keepNormals)
                cloudLabels.push_back(Label("normals", dimNormals));
        if (keepDensities)
                cloudLabels.push_back(Label("densities", dimDensities));
        if (keepEigenValues)
                cloudLabels.push_back(Label("eigValues", dimEigValues));
        if (keepEigenVectors)
                cloudLabels.push_back(Label("eigVectors", dimEigVectors));
        cloud.allocateDescriptors(cloudLabels);
 
        // we keep build data on stack for reentrant behaviour
        View cloudExistingDescriptors(cloud.descriptors.block(0,0,cloud.descriptors.rows(),cloud.descriptors.cols()));
        BuildData buildData(cloud.features, cloud.descriptors);
 
        // get views
        if (keepNormals)
                buildData.normals = cloud.getDescriptorViewByName("normals");
        if (keepDensities)
                buildData.densities = cloud.getDescriptorViewByName("densities");
        if (keepEigenValues)
                buildData.eigenValues = cloud.getDescriptorViewByName("eigValues");
        if (keepEigenVectors)
                buildData.eigenVectors = cloud.getDescriptorViewByName("eigVectors");
        // build the new point cloud
        buildNew(
                buildData,
                0,
                pointsCount,
                cloud.features.rowwise().minCoeff(),
                cloud.features.rowwise().maxCoeff()
        );
 
        // Bring the data we keep to the front of the arrays then
        // wipe the leftover unused space.
        std::sort(buildData.indicesToKeep.begin(), buildData.indicesToKeep.end());
        const int ptsOut = buildData.indicesToKeep.size();
        for (int i = 0; i < ptsOut; ++i)
        {
                const int k = buildData.indicesToKeep[i];
                assert(i <= k);
                cloud.features.col(i) = cloud.features.col(k);
                if (cloud.descriptors.rows() != 0)
                        cloud.descriptors.col(i) = cloud.descriptors.col(k);
                if (cloud.times.rows() != 0)
                        cloud.times.col(i) = cloud.times.col(k);
                if(keepNormals)
                        buildData.normals->col(i) = buildData.normals->col(k);
                if(keepDensities)
                        (*buildData.densities)(0,i) = (*buildData.densities)(0,k);
                if(keepEigenValues)
                        buildData.eigenValues->col(i) = buildData.eigenValues->col(k);
                if(keepEigenVectors)
                        buildData.eigenVectors->col(i) = buildData.eigenVectors->col(k);
        }
        cloud.conservativeResize(ptsOut);
        // warning if some points were dropped
        if(buildData.unfitPointsCount != 0)
                LOG_INFO_STREAM("  SamplingSurfaceNormalDataPointsFilter - Could not compute normal for " << buildData.unfitPointsCount << " pts.");
}
 
template<typename T>
void SamplingSurfaceNormalDataPointsFilter<T>::buildNew(
        BuildData& data, const int first, const int last, 
        Vector&& minValues, Vector&& maxValues) const
{
        using namespace PointMatcherSupport;
        
        const int count(last - first);
        if (count <= int(knn))
        {
                // compute for this range
                fuseRange(data, first, last);
                // TODO: make another filter that creates constant-density clouds,
                // typically by stopping recursion after the median of the bounding cuboid
                // is below a threshold, or that the number of points falls under a threshold
                return;
        }
 
        // find the largest dimension of the box
        const int cutDim = argMax<T>(maxValues - minValues);
 
        // compute number of elements
        const int rightCount(count/2);
        const int leftCount(count - rightCount);
        assert(last - rightCount == first + leftCount);
 
        // sort, hack std::nth_element
        std::nth_element(
                data.indices.begin() + first,
                data.indices.begin() + first + leftCount,
                data.indices.begin() + last,
                CompareDim(cutDim, data)
        );
 
        // get value
        const int cutIndex(data.indices[first+leftCount]);
        const T cutVal(data.features(cutDim, cutIndex));
 
        // update bounds for left
        Vector leftMaxValues(maxValues);
        leftMaxValues[cutDim] = cutVal;
        // update bounds for right
        Vector rightMinValues(minValues);
        rightMinValues[cutDim] = cutVal;
 
        // recurse
        buildNew(data, first, first + leftCount, 
                std::forward<Vector>(minValues), std::move(leftMaxValues));
        buildNew(data, first + leftCount, last, 
                std::move(rightMinValues), std::forward<Vector>(maxValues));
}
 
template<typename T>
void SamplingSurfaceNormalDataPointsFilter<T>::fuseRange(
        BuildData& data, const int first, const int last) const
{
        using namespace PointMatcherSupport;
        
        const int colCount(last-first);
        const int featDim(data.features.rows());
 
        // build nearest neighbors list
        Matrix d(featDim-1, colCount);
        for (int i = 0; i < colCount; ++i)
                d.col(i) = data.features.block(0,data.indices[first+i],featDim-1, 1);
        const Vector box = d.rowwise().maxCoeff() - d.rowwise().minCoeff();
        const T boxDim(box.maxCoeff());
        // drop box if it is too large
        if (boxDim > maxBoxDim)
        {
                data.unfitPointsCount += colCount;
                return;
        }
        const Vector mean = d.rowwise().sum() / T(colCount);
        const Matrix NN = (d.colwise() - mean);
 
        // compute covariance
        const Matrix C((NN * NN.transpose()) / T(colCount));
        Vector eigenVa = Vector::Identity(featDim-1, 1);
        Matrix eigenVe = Matrix::Identity(featDim-1, featDim-1);
        // Ensure that the matrix is suited for eigenvalues calculation
        if(keepNormals || keepEigenValues || keepEigenVectors)
        {
                if(C.fullPivHouseholderQr().rank()+1 >= featDim-1)
                {
                        const Eigen::EigenSolver<Matrix> solver(C);
                        eigenVa = solver.eigenvalues().real();
                        eigenVe = solver.eigenvectors().real();
                }
                else
                {
                        data.unfitPointsCount += colCount;
                        return;
                }
        }
 
        Vector normal;
        if(keepNormals)
                normal = computeNormal<T>(eigenVa, eigenVe);
 
        T densitie = 0;
        if(keepDensities)
                densitie = computeDensity<T>(NN);
 
        //if(keepEigenValues) nothing to do
 
        Vector serialEigVector;
        if(keepEigenVectors)
                serialEigVector = serializeEigVec<T>(eigenVe);
 
        // some safety check
        if(data.descriptors.rows() != 0)
                assert(data.descriptors.cols() != 0);
 
        // Filter points randomly
        if(samplingMethod == 0)
        {
                for(int i=0; i<colCount; ++i)
                {
                        const float r = (float)std::rand()/(float)RAND_MAX;
                        if(r < ratio)
                        {
                                // Keep points with their descriptors
                                const int k = data.indices[first+i];
                                // Mark the indices which will be part of the final data
                                data.indicesToKeep.push_back(k);
 
                                // Build new descriptors
                                if(keepNormals)
                                        data.normals->col(k) = normal;
                                if(keepDensities)
                                        (*data.densities)(0,k) = densitie;
                                if(keepEigenValues)
                                        data.eigenValues->col(k) = eigenVa;
                                if(keepEigenVectors)
                                        data.eigenVectors->col(k) = serialEigVector;
                        }
                }
        }
        else
        {
                const int k = data.indices[first];
                // Mark the indices which will be part of the final data
                data.indicesToKeep.push_back(k);
                data.features.col(k).topRows(featDim-1) = mean;
                data.features(featDim-1, k) = 1;
 
                if(data.descriptors.rows() != 0)
                {
                        // average the existing descriptors
                        if (averageExistingDescriptors)
                        {
                                Vector mergedDesc(Vector::Zero(data.descriptors.rows()));
                                for (int i = 0; i < colCount; ++i)
                                        mergedDesc += data.descriptors.col(data.indices[first+i]);
                                mergedDesc /= T(colCount);
                                data.descriptors.col(k) = mergedDesc;
                        }
                        // else just keep the first one
                }
 
                // Build new descriptors
                if(keepNormals)
                        data.normals->col(k) = normal;
                if(keepDensities)
                        (*data.densities)(0,k) = densitie;
                if(keepEigenValues)
                        data.eigenValues->col(k) = eigenVa;
                if(keepEigenVectors)
                        data.eigenVectors->col(k) = serialEigVector;
        }
}
 
template struct SamplingSurfaceNormalDataPointsFilter<float>;
template struct SamplingSurfaceNormalDataPointsFilter<double>;