OpenGV
A library for solving calibrated central and non-central geometric vision problems
Public Types | Public Member Functions | List of all members
opengv::sac::MultiRansac< PROBLEM_T > Class Template Reference

#include <MultiRansac.hpp>

Inheritance diagram for opengv::sac::MultiRansac< PROBLEM_T >:
opengv::sac::MultiSampleConsensus< PROBLEM_T >

Public Types

typedef PROBLEM_T problem_t
 
typedef problem_t::model_t model_t
 
- Public Types inherited from opengv::sac::MultiSampleConsensus< PROBLEM_T >
typedef PROBLEM_T problem_t
 
typedef problem_t::model_t model_t
 

Public Member Functions

 MultiRansac (int maxIterations=1000, double threshold=1.0, double probability=0.99)
 Constructor.
 
virtual ~MultiRansac ()
 Destructor.
 
bool computeModel (int debug_verbosity_level=0)
 Fit the model.
 
- Public Member Functions inherited from opengv::sac::MultiSampleConsensus< PROBLEM_T >
 MultiSampleConsensus (int maxIterations=1000, double threshold=1.0, double probability=0.99)
 Constructor. More...
 
virtual ~MultiSampleConsensus ()
 Destructor.
 

Additional Inherited Members

- Public Attributes inherited from opengv::sac::MultiSampleConsensus< PROBLEM_T >
int max_iterations_
 
int iterations_
 
double threshold_
 
double probability_
 
model_t model_coefficients_
 
std::vector< std::vector< int > > model_
 
std::vector< std::vector< int > > inliers_
 
std::shared_ptr< PROBLEM_T > sac_model_
 

Detailed Description

template<typename PROBLEM_T>
class opengv::sac::MultiRansac< PROBLEM_T >

The Ransac sample consensus method, as outlined in [15]. This one is using multi-indices for homogeneous sampling over groups of samples.

Member Typedef Documentation

template<typename PROBLEM_T >
typedef problem_t::model_t opengv::sac::MultiRansac< PROBLEM_T >::model_t

The model we trying to fit

template<typename PROBLEM_T >
typedef PROBLEM_T opengv::sac::MultiRansac< PROBLEM_T >::problem_t

A child of MultiSampleConsensusProblem

The documentation for this class was generated from the following file: