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Merge pull request #2455 from helix-re/filter/localfeature
Local feature filter
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| .. _filters.covariancefeatures: | ||
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| =============================================================================== | ||
| filters.covariancefeatures | ||
| =============================================================================== | ||
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| This filter implements various local feature descriptors introduced that are based on the covariance matrix of a | ||
| point's neighborhood. The user can pick a set of feature descriptors by | ||
| setting the ``feature_set`` option. Currently, the only supported feature is the dimensionality_ | ||
| set of feature descriptors introduced below. | ||
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| Example | ||
| ------------------------------------------------------------------------------- | ||
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| .. code-block:: json | ||
| [ | ||
| "input.las", | ||
| { | ||
| "type":"filters.covariancefeatures", | ||
| "knn":8, | ||
| "threads": 2, | ||
| "feature_set": "Dimensionality" | ||
| }, | ||
| { | ||
| "type":"writers.bpf", | ||
| "filename":"output.las", | ||
| "output_dims":"X,Y,Z,Linearity,Planarity,Scattering,Verticality" | ||
| } | ||
| ] | ||
| Options | ||
| ------------------------------------------------------------------------------- | ||
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| knn | ||
| The number of k nearest neighbors used for calculating the covariance matrix. [Default: 10] | ||
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| threads | ||
| The number of threads used for computing the feature descriptors. [Default: 1] | ||
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| feature_set | ||
| The features to be computed. Currently only supports ``Dimensionality``. [Default: "Dimensionality"] | ||
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| .. _dimensionality: | ||
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| Dimensionality feature set | ||
| ................................................................................ | ||
| The features introduced in [Demantke2011]_ describe the shape | ||
| of the neighborhood, indicating whether | ||
| the local geometry is more linear (1D), planar (2D) or volumetric (3D) while the one introduced in | ||
| [Guinard2017]_ adds the idea of a structure being vertical. | ||
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| The dimensionality filter introduces the following four descriptors that are computed from the covariance matrix of the ``knn`` neighbors: | ||
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| * linearity - higher for long thin strips | ||
| * planarity - higher for planar surfaces | ||
| * scattering - higher for complex 3d neighbourhoods | ||
| * verticality - higher for vertical structures, highest for thin vertical strips | ||
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| It introduces four new dimensions that hold each one of these values: ``Linearity`` ``Planarity`` ``Scattering`` | ||
| and ``Verticality``. | ||
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| /****************************************************************************** | ||
| * Copyright (c) 2019, Helix Re Inc. nicolas@helix.re | ||
| * | ||
| * 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 Helix Re Inc. 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 THE | ||
| * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, | ||
| * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, | ||
| * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS | ||
| * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED | ||
| * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT | ||
| * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY | ||
| * OF SUCH DAMAGE. | ||
| ****************************************************************************/ | ||
|
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| // This is an implementation of the local feature descriptors introduced in | ||
| // WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBANSCENES FROM 3D LIDAR POINT CLOUDS | ||
| // Stéphane Guinard, Loïc Landrieu, 2017 | ||
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| #include "CovarianceFeaturesFilter.hpp" | ||
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| #include <pdal/EigenUtils.hpp> | ||
| #include <pdal/KDIndex.hpp> | ||
| #include <pdal/util/ProgramArgs.hpp> | ||
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| #include <Eigen/Dense> | ||
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| #include <string> | ||
| #include <vector> | ||
| #include <cmath> | ||
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| namespace pdal | ||
| { | ||
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| static StaticPluginInfo const s_info | ||
| { | ||
| "filters.covariancefeatures", | ||
| "Filter that calculates local features based on the covariance matrix of a point's neighborhood.", | ||
| "http://pdal.io/stages/filters.covariancefeatures.html" | ||
| }; | ||
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| CREATE_STATIC_STAGE(CovarianceFeaturesFilter, s_info) | ||
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| std::string CovarianceFeaturesFilter::getName() const | ||
| { | ||
| return s_info.name; | ||
| } | ||
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| void CovarianceFeaturesFilter::addArgs(ProgramArgs& args) | ||
| { | ||
| args.add("knn", "k-Nearest neighbors", m_knn, 10); | ||
| args.add("threads", "Number of threads used to run this filter", m_threads, 1); | ||
| args.add("feature_set", "Set of features to be computed", m_featureSet, "Dimensionality"); | ||
| } | ||
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| void CovarianceFeaturesFilter::addDimensions(PointLayoutPtr layout) | ||
| { | ||
| if (m_featureSet == "Dimensionality") | ||
| { | ||
| for (auto dim: {"Linearity", "Planarity", "Scattering", "Verticality"}) | ||
| m_extraDims[dim] = layout->registerOrAssignDim(dim, Dimension::Type::Float); | ||
| } | ||
| } | ||
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| void CovarianceFeaturesFilter::filter(PointView& view) | ||
| { | ||
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| KD3Index& kdi = view.build3dIndex(); | ||
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| point_count_t nloops = view.size(); | ||
| std::vector<std::thread> threadPool(m_threads); | ||
| for(int t = 0;t<m_threads;t++) | ||
| { | ||
| threadPool[t] = std::thread(std::bind( | ||
| [&](const PointId start, const PointId end, const PointId t) | ||
| { | ||
| for(PointId i = start;i<end;i++) | ||
| setDimensionality(view, i, kdi); | ||
| }, | ||
| t*nloops/m_threads,(t+1)==m_threads?nloops:(t+1)*nloops/m_threads,t)); | ||
| } | ||
| for (auto &t: threadPool) | ||
| t.join(); | ||
| } | ||
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| void CovarianceFeaturesFilter::setDimensionality(PointView &view, const PointId &id, const KD3Index &kid) | ||
| { | ||
| using namespace Eigen; | ||
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| // find the k-nearest neighbors | ||
| auto ids = kid.neighbors(id, m_knn + 1); | ||
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| // compute covariance of the neighborhood | ||
| auto B = eigen::computeCovariance(view, ids); | ||
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| // perform the eigen decomposition | ||
| SelfAdjointEigenSolver<Matrix3f> solver(B); | ||
| if (solver.info() != Success) | ||
| throwError("Cannot perform eigen decomposition."); | ||
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| // Extract eigenvalues and eigenvectors in decreasing order (largest eigenvalue first) | ||
| auto ev = solver.eigenvalues(); | ||
| std::vector<float> lambda = {(std::max(ev[2],0.f)), | ||
| (std::max(ev[1],0.f)), | ||
| (std::max(ev[0],0.f))}; | ||
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| if (lambda[0] == 0) | ||
| throwError("Eigenvalues are all 0. Can't compute local features."); | ||
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| auto eigenVectors = solver.eigenvectors(); | ||
| std::vector<float> v1(3), v2(3), v3(3); | ||
| for (int i=0; i < 3; i++) | ||
| { | ||
| v1[i] = eigenVectors.col(2)(i); | ||
| v2[i] = eigenVectors.col(1)(i); | ||
| v3[i] = eigenVectors.col(0)(i); | ||
| } | ||
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| float linearity = (sqrtf(lambda[0]) - sqrtf(lambda[1])) / sqrtf(lambda[0]); | ||
| float planarity = (sqrtf(lambda[1]) - sqrtf(lambda[2])) / sqrtf(lambda[0]); | ||
| float scattering = sqrtf(lambda[2]) / sqrtf(lambda[0]); | ||
| view.setField(m_extraDims["Linearity"], id, linearity); | ||
| view.setField(m_extraDims["Planarity"], id, planarity); | ||
| view.setField(m_extraDims["Scattering"], id, scattering); | ||
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| std::vector<float> unary_vector(3); | ||
| float norm = 0; | ||
| for (int i=0; i <3 ; i++) | ||
| { | ||
| unary_vector[i] = lambda[0] * fabsf(v1[i]) + lambda[1] * fabsf(v2[i]) + lambda[2] * fabsf(v3[i]); | ||
| norm += unary_vector[i] * unary_vector[i]; | ||
| } | ||
| norm = sqrtf(norm); | ||
| view.setField(m_extraDims["Verticality"], id, unary_vector[2] / norm); | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,66 @@ | ||
| /****************************************************************************** | ||
| * Copyright (c) 2019, Helix Re Inc. nicolas@helix.re | ||
| * | ||
| * 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 Helix Re Inc. 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 THE | ||
| * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, | ||
| * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, | ||
| * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS | ||
| * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED | ||
| * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT | ||
| * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY | ||
| * OF SUCH DAMAGE. | ||
| ****************************************************************************/ | ||
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| #pragma once | ||
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| #include <thread> | ||
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| #include <pdal/Filter.hpp> | ||
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| namespace pdal { | ||
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| class PDAL_DLL CovarianceFeaturesFilter: public Filter | ||
| { | ||
| public: | ||
| CovarianceFeaturesFilter() : Filter() {} | ||
| CovarianceFeaturesFilter &operator=(const CovarianceFeaturesFilter &) = delete; | ||
| CovarianceFeaturesFilter(const CovarianceFeaturesFilter &) = delete; | ||
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| std::string getName() const; | ||
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| private: | ||
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| int m_knn; | ||
| int m_threads; | ||
| std::string m_featureSet; | ||
| std::map<std::string,Dimension::Id> m_extraDims; | ||
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| virtual void addDimensions(PointLayoutPtr layout); | ||
| virtual void addArgs(ProgramArgs &args); | ||
| virtual void filter(PointView &view); | ||
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| void setDimensionality(PointView &view, const PointId &id, const KD3Index &kid); | ||
| }; | ||
| } | ||
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