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() |
pcl::SampleConsensus< PointT > |
inlinevirtual |