, including all inherited members.
| computeMedian(const PointCloudConstPtr &cloud, const boost::shared_ptr< std::vector< int > > &indices, Eigen::Vector4f &median) | pcl::MaximumLikelihoodSampleConsensus< PointT > | [protected] |
| computeMedianAbsoluteDeviation(const PointCloudConstPtr &cloud, const boost::shared_ptr< std::vector< int > > &indices, double sigma) | pcl::MaximumLikelihoodSampleConsensus< PointT > | [protected] |
| computeModel(int debug_verbosity_level=0) | pcl::MaximumLikelihoodSampleConsensus< PointT > | [virtual] |
| ConstPtr typedef | pcl::SampleConsensus< PointT > | |
| getDistanceThreshold() | pcl::SampleConsensus< PointT > | [inline] |
| getEMIterations() const | pcl::MaximumLikelihoodSampleConsensus< PointT > | [inline] |
| getInliers(std::vector< int > &inliers) | pcl::SampleConsensus< PointT > | [inline] |
| getMaxIterations() | pcl::SampleConsensus< PointT > | [inline] |
| getMinMax(const PointCloudConstPtr &cloud, const boost::shared_ptr< std::vector< int > > &indices, Eigen::Vector4f &min_p, Eigen::Vector4f &max_p) | pcl::MaximumLikelihoodSampleConsensus< PointT > | [protected] |
| getModel(std::vector< int > &model) | pcl::SampleConsensus< PointT > | [inline] |
| getModelCoefficients(Eigen::VectorXf &model_coefficients) | pcl::SampleConsensus< PointT > | [inline] |
| getProbability() | pcl::SampleConsensus< PointT > | [inline] |
| getRandomSamples(const boost::shared_ptr< std::vector< int > > &indices, size_t nr_samples, std::set< int > &indices_subset) | pcl::SampleConsensus< PointT > | [inline] |
| inliers_ | pcl::SampleConsensus< PointT > | [protected] |
| iterations_ | pcl::SampleConsensus< PointT > | [protected] |
| iterations_EM_ | pcl::MaximumLikelihoodSampleConsensus< PointT > | [private] |
| 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] |
| PointCloudConstPtr typedef | pcl::MaximumLikelihoodSampleConsensus< PointT > | [private] |
| probability_ | pcl::SampleConsensus< PointT > | [protected] |
| Ptr typedef | pcl::SampleConsensus< PointT > | |
| rnd() | pcl::SampleConsensus< PointT > | [inline, protected] |
| 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] |
| SampleConsensusModelPtr typedef | pcl::MaximumLikelihoodSampleConsensus< PointT > | [private] |
| setDistanceThreshold(double threshold) | pcl::SampleConsensus< PointT > | [inline] |
| setEMIterations(int iterations) | pcl::MaximumLikelihoodSampleConsensus< PointT > | [inline] |
| setMaxIterations(int max_iterations) | pcl::SampleConsensus< PointT > | [inline] |
| setProbability(double probability) | pcl::SampleConsensus< PointT > | [inline] |
| sigma_ | pcl::MaximumLikelihoodSampleConsensus< PointT > | [private] |
| threshold_ | pcl::SampleConsensus< PointT > | [protected] |
| ~SampleConsensus() | pcl::SampleConsensus< PointT > | [inline, virtual] |