OpenGV
A library for solving calibrated central and non-central geometric vision problems
MultiSampleConsensus.hpp
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2 * Authors: Laurent Kneip & Paul Furgale *
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30
31//Note: has been derived from ROS
32
43#ifndef OPENGV_SAC_MULTISAMPLECONSENSUS_HPP_
44#define OPENGV_SAC_MULTISAMPLECONSENSUS_HPP_
45
49namespace opengv
50{
54namespace sac
55{
56
61template<typename PROBLEM_T>
63{
64public:
66 typedef PROBLEM_T problem_t;
68 typedef typename problem_t::model_t model_t;
69
79 int maxIterations = 1000,
80 double threshold = 1.0,
81 double probability = 0.99 );
86
92 virtual bool computeModel( int debug_verbosity_level = 0 ) = 0;
93
94 // \todo accessors
95 //private:
96
108 std::vector< std::vector<int> > model_;
110 std::vector< std::vector<int> > inliers_;
112 std::shared_ptr<PROBLEM_T> sac_model_;
113};
114
115} // namespace sac
116} // namespace opengv
117
118#include "implementation/MultiSampleConsensus.hpp"
119
120#endif /* OPENGV_MULTISAMPLECONSENSUS_HPP_ */
Definition: MultiSampleConsensus.hpp:63
problem_t::model_t model_t
Definition: MultiSampleConsensus.hpp:68
virtual bool computeModel(int debug_verbosity_level=0)=0
Fit the model to the data.
int iterations_
Definition: MultiSampleConsensus.hpp:100
std::shared_ptr< PROBLEM_T > sac_model_
Definition: MultiSampleConsensus.hpp:112
MultiSampleConsensus(int maxIterations=1000, double threshold=1.0, double probability=0.99)
Constructor.
PROBLEM_T problem_t
Definition: MultiSampleConsensus.hpp:66
std::vector< std::vector< int > > model_
Definition: MultiSampleConsensus.hpp:108
double threshold_
Definition: MultiSampleConsensus.hpp:102
model_t model_coefficients_
Definition: MultiSampleConsensus.hpp:106
std::vector< std::vector< int > > inliers_
Definition: MultiSampleConsensus.hpp:110
int max_iterations_
Definition: MultiSampleConsensus.hpp:98
double probability_
Definition: MultiSampleConsensus.hpp:104
virtual ~MultiSampleConsensus()
Destructor.
The namespace of this library.
Definition: AbsoluteAdapterBase.hpp:48