NormalSpace.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
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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*/
#include "NormalSpace.h"
 
#include <algorithm>
#include <vector>
#include <unordered_map>
#include <random>
#include <ciso646>
#include <cmath>
#include <numeric>
 
// NormalSpaceDataPointsFilter
template <typename T>
NormalSpaceDataPointsFilter<T>::NormalSpaceDataPointsFilter(const Parameters& params) :
        PointMatcher<T>::DataPointsFilter("NormalSpaceDataPointsFilter", 
                NormalSpaceDataPointsFilter::availableParameters(), params),
        nbSample{Parametrizable::get<std::size_t>("nbSample")},
        seed{Parametrizable::get<std::size_t>("seed")},
        epsilon{Parametrizable::get<T>("epsilon")},
        nbBucket{std::size_t(ceil(2.0 * M_PI / epsilon) * ceil(M_PI / epsilon))}
{
}
 
template <typename T>
typename PointMatcher<T>::DataPoints
NormalSpaceDataPointsFilter<T>::filter(const DataPoints& input)
{
        DataPoints output(input);
        inPlaceFilter(output);
        return output;
}
 
//TODO: Add support for 2D by building histogram of polar coordinate with uniform sampling
 
template <typename T>
void NormalSpaceDataPointsFilter<T>::inPlaceFilter(DataPoints& cloud)
{
        //check dimension
        const std::size_t featDim = cloud.features.rows();
        if(featDim < 4) //3D case support only
        {
                std::cerr << "ERROR: NormalSpaceDataPointsFilter does not support 2D point cloud yet (does nothing)" << std::endl;
                return;
        }
                
        //Check number of points
        const int nbPoints = cloud.getNbPoints();               
        if(nbSample >= std::size_t(nbPoints))
                return;
 
        //Check if there is normals info
        if (!cloud.descriptorExists("normals"))
                throw InvalidField("OrientNormalsDataPointsFilter: Error, cannot find normals in descriptors.");
 
        const auto& normals = cloud.getDescriptorViewByName("normals");
        
        std::mt19937 gen(seed); //Standard mersenne_twister_engine seeded with seed
 
        //bucketed normal space
        std::vector<std::vector<int> > idBuckets;
        idBuckets.resize(nbBucket);
        
        std::vector<std::size_t> keepIndexes;
        keepIndexes.reserve(nbSample);
 
        // Generate a random sequence of indices so that elements are placed in buckets in random order
        std::vector<std::size_t> randIdcs(nbPoints);
        std::iota(randIdcs.begin(), randIdcs.end(), 0);
        std::random_shuffle(randIdcs.begin(), randIdcs.end());
 
        for (auto randIdx : randIdcs)
        {
                // Allow for slight approximiation errors
                assert(normals.col(randIdx).head(3).norm() >= 1.0-0.00001);
                assert(normals.col(randIdx).head(3).norm() <= 1.0+0.00001);
                // Catch errors where theta will be NaN
                assert((normals(2,randIdx) <= 1.0) && (normals(2,randIdx) >= -1.0));
 
                //Theta = polar angle in [0 ; pi]
                const T theta = std::acos(normals(2, randIdx));
                //Phi = azimuthal angle in [0 ; 2pi]
                const T phi = std::fmod(std::atan2(normals(1, randIdx), normals(0, randIdx)) + 2. * M_PI, 2. * M_PI);
 
                // Catch normal space hashing errors
                assert(bucketIdx(theta, phi) < nbBucket);
                idBuckets[bucketIdx(theta, phi)].push_back(randIdx);
        }
 
        // Remove empty buckets
        idBuckets.erase(std::remove_if(idBuckets.begin(), idBuckets.end(),
                                [](const std::vector<int>& bucket) { return bucket.empty(); }),
                                idBuckets.end());
 
        for (std::size_t i=0; i<nbSample; i++)
        {
                // Get a random bucket
                std::uniform_int_distribution<std::size_t> uniBucket(0,idBuckets.size()-1);
                std::size_t curBucketIdx = uniBucket(gen);
                std::vector<int>& curBucket = idBuckets[curBucketIdx];
 
                int idToKeep = curBucket[curBucket.size()-1];
                curBucket.pop_back();
                keepIndexes.push_back(static_cast<std::size_t>(idToKeep));
 
                // Remove the bucket if it is empty
                if (curBucket.empty()) {
                        idBuckets.erase(idBuckets.begin()+curBucketIdx);
                }
        }
 
        //TODO: evaluate performances between this solution and sorting the indexes
        // We build map of (old index to new index), in case we sample pts at the begining of the pointcloud
        std::unordered_map<std::size_t, std::size_t> mapidx;
        std::size_t idx = 0;
        
        for(std::size_t id : keepIndexes)
        {
                //retrieve index from lookup table if sampling in already switched element
                if(id<idx)
                        id = mapidx[id];
                //Switch columns id and idx
                cloud.swapCols(idx, id);        
                //Maintain new index position   
                mapidx[idx] = id;
                //Update index
                ++idx;
        }
        cloud.conservativeResize(nbSample);
}
 
template <typename T>
std::size_t NormalSpaceDataPointsFilter<T>::bucketIdx(T theta, T phi) const
{
        //Theta = polar angle in [0 ; pi] and Phi = azimuthal angle in [0 ; 2pi]
        assert( (theta >= 0.0) && (theta <= static_cast<T>(M_PI)) && "Theta not in [0, Pi]");
        assert( (phi >= 0) && (phi <= 2*static_cast<T>(M_PI)) && "Phi not in [0, 2Pi]");
 
        // Wrap Theta at Pi
        if (theta == static_cast<T>(M_PI)) { theta = 0.0; };
        // Wrap Phi at 2Pi
        if (phi == 2*static_cast<T>(M_PI)) { phi = 0.0; };
        //                               block number           block size               element number
        return static_cast<std::size_t>( floor(theta/epsilon) * ceil(2.0*M_PI/epsilon) + floor(phi/epsilon) );
}
 
template struct NormalSpaceDataPointsFilter<float>;
template struct NormalSpaceDataPointsFilter<double>;