KdTree with arbitrary dimension. More...

#include <kdtreex.hpp>

Inheritance diagram for gtsam_points::KdTreeX< D >:

Collaboration diagram for gtsam_points::KdTreeX< D >:

Public Types
using	Index = nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Simple_Adaptor< double, KdTreeX< D >, double >, KdTreeX< D >, D, size_t >

Public Types inherited from gtsam_points::NearestNeighborSearch
using	Ptr = std::shared_ptr< NearestNeighborSearch >

using	ConstPtr = std::shared_ptr< const NearestNeighborSearch >

Public Member Functions
	KdTreeX (const Eigen::Matrix< double, D, 1 > *points, int num_points)
	Constructor.

int	dim () const

size_t	kdtree_get_point_count () const

double	kdtree_get_pt (const size_t idx, const size_t dim) const

template<class BBox >
bool	kdtree_get_bbox (BBox &) const

virtual size_t	knn_search (const double pt, size_t k, size_t k_indices, double *k_sq_dists, double max_sq_dist=std::numeric_limits< double >::max()) const override
	k-nearest neighbor search

virtual size_t	radius_search (const double *pt, double radius, std::vector< size_t > &indices, std::vector< double > &sq_dists, int max_num_neighbors=std::numeric_limits< int >::max()) const override
	Radius search.

Detailed Description

template<int D = -1>
struct gtsam_points::KdTreeX< D >

KdTree with arbitrary dimension.

Member Function Documentation

template<int D>

size_t gtsam_points::KdTreeX< D >::knn_search	(	const double *	pt,
		size_t	k,
		size_t *	k_indices,
		double *	k_sq_dists,
		double	max_sq_dist = `std::numeric_limits<double>::max()`
	)		const

overridevirtual

k-nearest neighbor search

Parameters

pt	Point
k	Number of neighbors
k_indices	Indices of k-nearest neighbors
k_sq_dists	Squared distances to the neighbors (sorted in ascending order)

template<int D>

size_t gtsam_points::KdTreeX< D >::radius_search	(	const double *	pt,
		double	radius,
		std::vector< size_t > &	indices,
		std::vector< double > &	sq_dists,
		int	max_num_neighbors = `std::numeric_limits<int>::max()`
	)		const

overridevirtual

Radius search.

Note: There is no assumption and guarantee on the order of points to be selected when max_num_neighbors is specified. (Some algorithms like KdTree tend to first pick closer points though).

Parameters

pt	Point
radius	Search radius
indices	Indices of neighbors within the radius
sq_dists	Squared distances to the neighbors (sorted in ascending order)
max_num_neighbors	Maximum number of neighbors

The documentation for this struct was generated from the following files:

/home/runner/work/gtsam_points/gtsam_points/include/gtsam_points/ann/kdtreex.hpp
/home/runner/work/gtsam_points/gtsam_points/include/gtsam_points/ann/impl/kdtreex_impl.hpp