Add methods to detect and remove the outliers from a point cloud
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/****************************************************************************
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* VCGLib o o *
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* Visual and Computer Graphics Library o o *
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* _ O _ *
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* Copyright(C) 2004-2015 \/)\/ *
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* Visual Computing Lab /\/| *
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* ISTI - Italian National Research Council | *
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* \ *
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* All rights reserved. *
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* *
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* This program is free software; you can redistribute it and/or modify *
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* it under the terms of the GNU General Public License as published by *
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* the Free Software Foundation; either version 2 of the License, or *
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* (at your option) any later version. *
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* *
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* This program is distributed in the hope that it will be useful, *
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* but WITHOUT ANY WARRANTY; without even the implied warranty of *
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
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* GNU General Public License (http://www.gnu.org/licenses/gpl.txt) *
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* for more details. *
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* *
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****************************************************************************/
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#ifndef VCG_TRI_OUTLIERS__H
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#define VCG_TRI_OUTLIERS_H
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#include <vcg/space/index/kdtree/kdtree.h>
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namespace vcg
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{
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namespace tri
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{
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template <class MeshType>
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class OutlierRemoval
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{
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public:
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typedef typename MeshType::ScalarType ScalarType;
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typedef typename vcg::KdTree<ScalarType> KdTreeType;
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typedef typename vcg::KdTree<ScalarType>::PriorityQueue PriorityQueue;
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/**
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Compute an outlier probability value for each vertex of the mesh using the approch
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in the paper "LoOP: Local Outlier Probabilities". The outlier probability is stored in the
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vertex attribute "outlierScore". It use the input kdtree to find the kNearest of each vertex.
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"LoOP: local outlier probabilities" by Hans-Peter Kriegel et al.
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Proceedings of the 18th ACM conference on Information and knowledge management
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*/
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static void ComputeLoOPScore(MeshType& mesh, KdTreeType& kdTree, int kNearest)
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{
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vcg::tri::RequireCompactness(mesh);
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CMesh::PerVertexAttributeHandle<ScalarType> outlierScore = vcg::tri::Allocator<MeshType>::GetPerVertexAttribute<ScalarType>(mesh, std::string("outlierScore"));
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CMesh::PerVertexAttributeHandle<ScalarType> sigma = vcg::tri::Allocator<MeshType>::GetPerVertexAttribute<ScalarType>(mesh, std::string("sigma"));
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CMesh::PerVertexAttributeHandle<ScalarType> plof = vcg::tri::Allocator<MeshType>::GetPerVertexAttribute<ScalarType>(mesh, std::string("plof"));
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#pragma omp parallel for schedule(dynamic, 10)
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for (int i = 0; i < mesh.vert.size(); i++)
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{
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PriorityQueue queue;
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kdTree.doQueryK(mesh.vert[i].cP(), kNearest, queue);
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ScalarType sum = 0;
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for (int j = 0; j < queue.getNofElements(); j++)
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sum += queue.getWeight(j);
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sum /= (queue.getNofElements());
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sigma[i] = sqrt(sum);
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}
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float mean = 0;
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#pragma omp parallel for reduction(+: mean) schedule(dynamic, 10)
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for (int i = 0; i < mesh.vert.size(); i++)
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{
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PriorityQueue queue;
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kdTree.doQueryK(mesh.vert[i].cP(), kNearest, queue);
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ScalarType sum = 0;
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for (int j = 0; j < queue.getNofElements(); j++)
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sum += sigma[queue.getIndex(j)];
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sum /= (queue.getNofElements());
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plof[i] = sigma[i] / sum - 1.0f;
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mean += plof[i] * plof[i];
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}
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mean /= mesh.vert.size();
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mean = sqrt(mean);
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#pragma omp parallel for schedule(dynamic, 10)
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for (int i = 0; i < mesh.vert.size(); i++)
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{
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ScalarType value = plof[i] / (mean * sqrt(2.0f));
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double dem = 1.0 + 0.278393 * value;
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dem += 0.230389 * value * value;
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dem += 0.000972 * value * value * value;
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dem += 0.078108 * value * value * value * value;
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ScalarType op = max(0.0, 1.0 - 1.0 / dem);
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outlierScore[i] = op;
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}
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vcg::tri::Allocator<CMesh>::DeletePerVertexAttribute(mesh, std::string("sigma"));
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vcg::tri::Allocator<CMesh>::DeletePerVertexAttribute(mesh, std::string("plof"));
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};
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/**
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Delete all the vertex of the mesh with an outlier probability above the input threshold [0.0, 1.0].
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*/
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static int DeleteLoOPOutliers(MeshType& mesh, KdTreeType& kdTree, int kNearest, float threshold)
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{
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ComputeLoOPScore(mesh, kdTree, kNearest);
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int count = 0;
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CMesh::PerVertexAttributeHandle<ScalarType> outlierScore = vcg::tri::Allocator<MeshType>::GetPerVertexAttribute<ScalarType>(mesh, std::string("outlierScore"));
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for (int i = 0; i < mesh.vert.size(); i++)
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{
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if (outlierScore[i] > threshold)
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{
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vcg::tri::Allocator<CMesh>::DeleteVertex(mesh, mesh.vert[i]);
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count++;
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}
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}
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vcg::tri::Allocator<CMesh>::CompactVertexVector(mesh);
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vcg::tri::Allocator<CMesh>::DeletePerVertexAttribute(mesh, std::string("outlierScore"));
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return count;
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};
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};
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} // end namespace tri
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} // end namespace vcg
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#endif // VCG_TRI_OUTLIERS_H
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