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computeMedian(const PointCloudConstPtr &cloud, const IndicesPtr &indices, Eigen::Vector4f &median) const |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
protected |
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computeMedianAbsoluteDeviation(const PointCloudConstPtr &cloud, const IndicesPtr &indices, double sigma) const |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
protected |
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computeModel(int debug_verbosity_level=0) override |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
virtual |
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ConstPtr typedef |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
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getDistanceThreshold() const |
pcl::SampleConsensus< PointT > |
inline |
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getEMIterations() const |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
inline |
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getInliers(Indices &inliers) const |
pcl::SampleConsensus< PointT > |
inline |
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getMaxIterations() const |
pcl::SampleConsensus< PointT > |
inline |
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getMinMax(const PointCloudConstPtr &cloud, const IndicesPtr &indices, Eigen::Vector4f &min_p, Eigen::Vector4f &max_p) const |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
protected |
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getModel(Indices &model) const |
pcl::SampleConsensus< PointT > |
inline |
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getModelCoefficients(Eigen::VectorXf &model_coefficients) const |
pcl::SampleConsensus< PointT > |
inline |
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getNumberOfThreads() const |
pcl::SampleConsensus< PointT > |
inline |
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getProbability() const |
pcl::SampleConsensus< PointT > |
inline |
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getRandomSamples(const IndicesPtr &indices, std::size_t nr_samples, std::set< index_t > &indices_subset) |
pcl::SampleConsensus< PointT > |
inline |
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getSampleConsensusModel() const |
pcl::SampleConsensus< PointT > |
inline |
| inliers_ |
pcl::SampleConsensus< PointT > |
protected |
| iterations_ |
pcl::SampleConsensus< PointT > |
protected |
| max_iterations_ |
pcl::SampleConsensus< PointT > |
protected |
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MaximumLikelihoodSampleConsensus(const SampleConsensusModelPtr &model) |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
inline |
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MaximumLikelihoodSampleConsensus(const SampleConsensusModelPtr &model, double threshold) |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
inline |
| model_ |
pcl::SampleConsensus< PointT > |
protected |
| model_coefficients_ |
pcl::SampleConsensus< PointT > |
protected |
| probability_ |
pcl::SampleConsensus< PointT > |
protected |
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Ptr typedef |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
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refineModel(const double sigma=3.0, const unsigned int max_iterations=1000) |
pcl::SampleConsensus< PointT > |
inlinevirtual
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rnd() |
pcl::SampleConsensus< PointT > |
inlineprotected
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| rng_ |
pcl::SampleConsensus< PointT > |
protected |
| rng_alg_ |
pcl::SampleConsensus< PointT > |
protected |
| sac_model_ |
pcl::SampleConsensus< PointT > |
protected |
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SampleConsensus(const SampleConsensusModelPtr &model, bool random=false) |
pcl::SampleConsensus< PointT > |
inline |
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SampleConsensus(const SampleConsensusModelPtr &model, double threshold, bool random=false) |
pcl::SampleConsensus< PointT > |
inline |
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setDistanceThreshold(double threshold) |
pcl::SampleConsensus< PointT > |
inline |
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setEMIterations(int iterations) |
pcl::MaximumLikelihoodSampleConsensus< PointT > |
inline |
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setMaxIterations(int max_iterations) |
pcl::SampleConsensus< PointT > |
inline |
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setNumberOfThreads(const int nr_threads=-1) |
pcl::SampleConsensus< PointT > |
inline |
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setProbability(double probability) |
pcl::SampleConsensus< PointT > |
inline |
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setSampleConsensusModel(const SampleConsensusModelPtr &model) |
pcl::SampleConsensus< PointT > |
inline |
| threads_ |
pcl::SampleConsensus< PointT > |
protected |
| threshold_ |
pcl::SampleConsensus< PointT > |
protected |
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~SampleConsensus() |
pcl::SampleConsensus< PointT > |
inlinevirtual
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