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