#include <kdTree.h>

Inheritance diagram for Ponca::KdTreeBase< Traits >:

Collaboration diagram for Ponca::KdTreeBase< Traits >:

Classes
struct	DefaultConverter
	Convert a custom point container to the KdTree PointContainer using DataPoint default constructor. More...

Public Types
using	DataPoint = typename Traits::DataPoint
	DataPoint given by user via Traits.

using	IndexType = typename Traits::IndexType
	Type used to index points into the PointContainer.

using	LeafSizeType = typename Traits::LeafSizeType
	Type used to store the size of leaf nodes.

using	PointContainer = typename Traits::PointContainer
	Container for DataPoint used inside the KdTree.

using	IndexContainer = typename Traits::IndexContainer
	Container for indices used inside the KdTree.

using	NodeIndexType = typename Traits::NodeIndexType
	Type used to index nodes into the NodeContainer.

using	NodeType = typename Traits::NodeType
	Type of nodes used inside the KdTree.

using	NodeContainer = typename Traits::NodeContainer
	Container for nodes used inside the KdTree.

using	Scalar = typename DataPoint::Scalar
	Scalar given by user via DataPoint.

using	VectorType = typename DataPoint::VectorType
	VectorType given by user via DataPoint.

using	AabbType = typename NodeType::AabbType
	Bounding box type given by user via NodeType.

Public Member Functions
template<typename PointUserContainer , typename Converter >
void	build (PointUserContainer &&points, Converter c)
	Generate a tree from a custom contained type converted using the specified converter.

template<typename PointUserContainer >
void	build (PointUserContainer &&points)
	Generate a tree from a custom contained type converted using DefaultConverter.

void	clear ()
	Clear tree data.

NodeIndexType	node_count () const

IndexType	sample_count () const

IndexType	pointCount () const

NodeIndexType	leaf_count () const

PointContainer &	points ()

const PointContainer &	points () const

const NodeContainer &	nodes () const

const IndexContainer &	samples () const

LeafSizeType	minCellSize () const
	Read leaf min size.

void	setMinCellSize (LeafSizeType min_cell_size)
	Write leaf min size.

IndexType	pointFromSample (IndexType sample_index) const
	Return the point index associated with the specified sample index.

DataPoint &	pointDataFromSample (IndexType sample_index)
	Return the DataPoint associated with the specified sample index.

const DataPoint &	pointDataFromSample (IndexType sample_index) const
	Return the DataPoint associated with the specified sample index.

KdTreeKNearestPointQuery< Traits >	kNearestNeighbors (const VectorType &point, IndexType k) const
	Computes a Query object to iterate over the k-nearest neighbors of a point. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

KdTreeKNearestIndexQuery< Traits >	kNearestNeighbors (IndexType index, IndexType k) const
	Computes a Query object to iterate over the k-nearest neighbors of a point. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

KdTreeKNearestPointQuery< Traits >	kNearestNeighborsQuery () const
	Convenience function that provides an empty k-nearest neighbors Query object.

KdTreeKNearestIndexQuery< Traits >	kNearestNeighborsIndexQuery () const
	Convenience function that provides an empty k-nearest neighbors Query object.

KdTreeNearestPointQuery< Traits >	nearestNeighbor (const VectorType &point) const
	Computes a Query object that contains the nearest point. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

KdTreeNearestIndexQuery< Traits >	nearestNeighbor (IndexType index) const
	Computes a Query object that contains the nearest point. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

KdTreeNearestIndexQuery< Traits >	nearestNeighborQuery () const
	Convenience function that provides an empty nearest neighbor Query object.

KdTreeNearestIndexQuery< Traits >	nearestNeighborIndexQuery () const
	Convenience function that provides an empty nearest neighbor Query object.

KdTreeRangePointQuery< Traits >	rangeNeighbors (const VectorType &point, Scalar r) const
	Computes a Query object to iterate over the neighbors that are inside a given radius. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

KdTreeRangeIndexQuery< Traits >	rangeNeighbors (IndexType index, Scalar r) const
	Computes a Query object to iterate over the neighbors that are inside a given radius. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

KdTreeRangePointQuery< Traits >	rangeNeighborsQuery () const
	Convenience function that provides an empty range neighbor Query object.

KdTreeRangeIndexQuery< Traits >	rangeNeighborsIndexQuery () const
	KdTreeBase::rangeNeighborsQuery.

bool	valid () const

void	print (std::ostream &os, bool verbose=false) const

template<typename PointUserContainer , typename Converter >
void	build (PointUserContainer &&points, Converter c)

template<typename PointUserContainer , typename IndexUserContainer , typename Converter >
void	buildWithSampling (PointUserContainer &&points, IndexUserContainer sampling, Converter c)

Static Public Attributes
static constexpr std::size_t	MAX_NODE_COUNT = NodeType::MAX_COUNT
	The maximum number of nodes that the kd-tree can have.

static constexpr std::size_t	MAX_POINT_COUNT = std::size_t(2) << sizeof(IndexType)*8
	The maximum number of points that can be stored in the kd-tree.

static constexpr int	MAX_DEPTH = Traits::MAX_DEPTH
	The maximum depth of the kd-tree.

static constexpr bool	SUPPORTS_SUBSAMPLING = false

Protected Member Functions
template<typename PointUserContainer , typename IndexUserContainer , typename Converter >
void	buildWithSampling (PointUserContainer &&points, IndexUserContainer sampling, Converter c)
	Generate a tree sampled from a custom contained type converted using a `Converter`

template<typename PointUserContainer , typename IndexUserContainer >
void	buildWithSampling (PointUserContainer &&points, IndexUserContainer sampling)
	Generate a tree sampled from a custom contained type converted using a KdTreeBase::DefaultConverter.

Protected Attributes
PointContainer	m_points

NodeContainer	m_nodes

IndexContainer	m_indices

LeafSizeType	m_min_cell_size {64}
	Minimal number of points per leaf.

NodeIndexType	m_leaf_count {0}
	Number of leaves in the Kdtree (computed during construction)

Detailed Description

template<typename Traits>
class Ponca::KdTreeBase< Traits >

[KdTreeSparse type definition]

Customizable base class for KdTree datastructure implementations

See also: Ponca::KdTreeDense; Ponca::KdTreeSparse

Template Parameters

Traits Traits type providing the types and constants used by the kd-tree. Must have the same interface as the default traits type.

See also: KdTreeDefaultTraits for the trait interface documentation.

Definition at line 103 of file kdTree.h.

Member Typedef Documentation

◆ AabbType

template<typename Traits >

using Ponca::KdTreeBase< Traits >::AabbType = typename NodeType::AabbType

Bounding box type given by user via NodeType.

Definition at line 117 of file kdTree.h.

◆ DataPoint

template<typename Traits >

using Ponca::KdTreeBase< Traits >::DataPoint = typename Traits::DataPoint

DataPoint given by user via Traits.

Definition at line 106 of file kdTree.h.

◆ IndexContainer

template<typename Traits >

using Ponca::KdTreeBase< Traits >::IndexContainer = typename Traits::IndexContainer

Container for indices used inside the KdTree.

Definition at line 110 of file kdTree.h.

◆ IndexType

template<typename Traits >

using Ponca::KdTreeBase< Traits >::IndexType = typename Traits::IndexType

Type used to index points into the PointContainer.

Definition at line 107 of file kdTree.h.

◆ LeafSizeType

template<typename Traits >

using Ponca::KdTreeBase< Traits >::LeafSizeType = typename Traits::LeafSizeType

Type used to store the size of leaf nodes.

Definition at line 108 of file kdTree.h.

◆ NodeContainer

template<typename Traits >

using Ponca::KdTreeBase< Traits >::NodeContainer = typename Traits::NodeContainer

Container for nodes used inside the KdTree.

Definition at line 113 of file kdTree.h.

◆ NodeIndexType

template<typename Traits >

using Ponca::KdTreeBase< Traits >::NodeIndexType = typename Traits::NodeIndexType

Type used to index nodes into the NodeContainer.

Definition at line 111 of file kdTree.h.

◆ NodeType

template<typename Traits >

using Ponca::KdTreeBase< Traits >::NodeType = typename Traits::NodeType

Type of nodes used inside the KdTree.

Definition at line 112 of file kdTree.h.

◆ PointContainer

template<typename Traits >

using Ponca::KdTreeBase< Traits >::PointContainer = typename Traits::PointContainer

Container for DataPoint used inside the KdTree.

Definition at line 109 of file kdTree.h.

◆ Scalar

template<typename Traits >

using Ponca::KdTreeBase< Traits >::Scalar = typename DataPoint::Scalar

Scalar given by user via DataPoint.

Definition at line 115 of file kdTree.h.

◆ VectorType

template<typename Traits >

using Ponca::KdTreeBase< Traits >::VectorType = typename DataPoint::VectorType

VectorType given by user via DataPoint.

Definition at line 116 of file kdTree.h.

Member Function Documentation

◆ build() [1/3]

template<typename Traits >

template<typename PointUserContainer >

void Ponca::KdTreeBase< Traits >::build ( PointUserContainer && points )

inline

Generate a tree from a custom contained type converted using DefaultConverter.

Template Parameters

PointUserContainer Input point container, transformed to PointContainer

Parameters

points Input points

Definition at line 169 of file kdTree.h.

◆ build() [2/3]

template<typename Traits >

template<typename PointUserContainer , typename Converter >

void Ponca::KdTreeBase< Traits >::build	(	PointUserContainer &&	points,
		Converter	c
	)

inline

Generate a tree from a custom contained type converted using the specified converter.

Template Parameters

PointUserContainer	Input point container, transformed to PointContainer
IndexUserContainer	Input sampling container, transformed to IndexContainer

Parameters

points	Input points
c	Cast/Convert input point type to DataType

◆ build() [3/3]

template<typename Traits >

template<typename PointUserContainer , typename Converter >

void Ponca::KdTreeBase< Traits >::build	(	PointUserContainer &&	points,
		Converter	c
	)

inline

Definition at line 11 of file kdTree.hpp.

◆ buildWithSampling() [1/3]

template<typename Traits >

template<typename PointUserContainer , typename IndexUserContainer >

void Ponca::KdTreeBase< Traits >::buildWithSampling	(	PointUserContainer &&	points,
		IndexUserContainer	sampling
	)

inlineprotected

Generate a tree sampled from a custom contained type converted using a KdTreeBase::DefaultConverter.

Template Parameters

PointUserContainer	Input points, transformed to PointContainer
IndexUserContainer	Input sampling, transformed to IndexContainer

Parameters

points	Input points
sampling	Samples used in the tree

Definition at line 446 of file kdTree.h.

◆ buildWithSampling() [2/3]

template<typename Traits >

template<typename PointUserContainer , typename IndexUserContainer , typename Converter >

void Ponca::KdTreeBase< Traits >::buildWithSampling	(	PointUserContainer &&	points,
		IndexUserContainer	sampling,
		Converter	c
	)

inlineprotected

Generate a tree sampled from a custom contained type converted using a Converter

Template Parameters

PointUserContainer	Input point, transformed to PointContainer
IndexUserContainer	Input sampling, transformed to IndexContainer
Converter

Parameters

points	Input points
sampling	Indices of points used in the tree
c	Cast/Convert input point type to DataType

◆ buildWithSampling() [3/3]

template<typename Traits >

template<typename PointUserContainer , typename IndexUserContainer , typename Converter >

void Ponca::KdTreeBase< Traits >::buildWithSampling	(	PointUserContainer &&	points,
		IndexUserContainer	sampling,
		Converter	c
	)

inline

Definition at line 121 of file kdTree.hpp.

◆ clear()

template<typename Traits >

void KdTreeBase::clear ( )

inline

Clear tree data.

Definition at line 19 of file kdTree.hpp.

◆ kNearestNeighbors() [1/2]

template<typename Traits >

KdTreeKNearestPointQuery< Traits > Ponca::KdTreeBase< Traits >::kNearestNeighbors	(	const VectorType &	point,
		IndexType	k
	)		const

inline

Computes a Query object to iterate over the k-nearest neighbors of a point. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

Parameters

point	Point from where the query is evaluated
k	Number of neighbors returned

Returns: The KdTreeKNearestIndexQuery mutable object to iterate over the search results.

See also: KdTreeKNearestQueryBase

Definition at line 268 of file kdTree.h.

◆ kNearestNeighbors() [2/2]

template<typename Traits >

KdTreeKNearestIndexQuery< Traits > Ponca::KdTreeBase< Traits >::kNearestNeighbors	(	IndexType	index,
		IndexType	k
	)		const

inline

Computes a Query object to iterate over the k-nearest neighbors of a point. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

Parameters

index	Index of the point from where the query is evaluated
k	Number of neighbors returned

Returns: The KdTreeKNearestIndexQuery mutable object to iterate over the search results.

See also: KdTreeKNearestQueryBase

Definition at line 278 of file kdTree.h.

◆ kNearestNeighborsIndexQuery()

template<typename Traits >

KdTreeKNearestIndexQuery< Traits > Ponca::KdTreeBase< Traits >::kNearestNeighborsIndexQuery ( ) const

inline

Convenience function that provides an empty k-nearest neighbors Query object.

The returned object can call for a k-nearest neighbors search using the operator (), which takes a k and an index as parameters.

Same as KdTreeBase::kNearestNeighbors (0, 0)

Returns: The empty KdTreeKNearestIndexQuery mutable object to iterate over the search results.

See also: KdTreeKNearestQueryBase

Definition at line 304 of file kdTree.h.

◆ kNearestNeighborsQuery()

template<typename Traits >

KdTreeKNearestPointQuery< Traits > Ponca::KdTreeBase< Traits >::kNearestNeighborsQuery ( ) const

inline

Convenience function that provides an empty k-nearest neighbors Query object.

The returned object can call for a k-nearest neighbors search using the operator (), which takes a k and a position as parameters.

Same as KdTreeBase::kNearestNeighbors (0, VectorType::Zero())

Returns: The empty KdTreeKNearestPointQuery mutable object to iterate over the search results.

See also: KdTreeKNearestQueryBase

Definition at line 291 of file kdTree.h.

◆ leaf_count()

template<typename Traits >

NodeIndexType Ponca::KdTreeBase< Traits >::leaf_count ( ) const

inline

Definition at line 194 of file kdTree.h.

◆ minCellSize()

template<typename Traits >

LeafSizeType Ponca::KdTreeBase< Traits >::minCellSize ( ) const

inline

Read leaf min size.

Definition at line 222 of file kdTree.h.

◆ nearestNeighbor() [1/2]

template<typename Traits >

KdTreeNearestPointQuery< Traits > Ponca::KdTreeBase< Traits >::nearestNeighbor ( const VectorType & point ) const

inline

Computes a Query object that contains the nearest point. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

Parameters

point Point from where the query is evaluated

Returns: The KdTreeNearestPointQuery mutable object that contains the search result.

See also: KdTreeNearestQueryBase

Definition at line 316 of file kdTree.h.

◆ nearestNeighbor() [2/2]

template<typename Traits >

KdTreeNearestIndexQuery< Traits > Ponca::KdTreeBase< Traits >::nearestNeighbor ( IndexType index ) const

inline

Computes a Query object that contains the nearest point. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

Parameters

index Index of the point from where the query is evaluated

Returns: The KdTreeKNearestIndexQuery mutable object that contains the search result.

See also: KdTreeNearestQueryBase

Definition at line 326 of file kdTree.h.

◆ nearestNeighborIndexQuery()

template<typename Traits >

KdTreeNearestIndexQuery< Traits > Ponca::KdTreeBase< Traits >::nearestNeighborIndexQuery ( ) const

inline

Convenience function that provides an empty nearest neighbor Query object.

The returned object can call for a nearest neighbor search using the operator (), which takes an index as parameter.

Same as KdTreeBase::nearestNeighbor (0)

Returns: The KdTreeKNearestIndexQuery mutable object that contains the search result.

See also: KdTreeNearestQueryBase

Definition at line 354 of file kdTree.h.

◆ nearestNeighborQuery()

template<typename Traits >

KdTreeNearestIndexQuery< Traits > Ponca::KdTreeBase< Traits >::nearestNeighborQuery ( ) const

inline

Convenience function that provides an empty nearest neighbor Query object.

The returned object can call for a nearest neighbor search using the operator (), which takes a position as parameter.

Same as KdTreeBase::nearestNeighbor (VectorType::Zero())

Returns: The empty KdTreeNearestPointQuery mutable object that contains the search result.

See also: KdTreeNearestQueryBase

Definition at line 340 of file kdTree.h.

◆ node_count()

template<typename Traits >

NodeIndexType Ponca::KdTreeBase< Traits >::node_count ( ) const

inline

Definition at line 179 of file kdTree.h.

◆ nodes()

template<typename Traits >

const NodeContainer & Ponca::KdTreeBase< Traits >::nodes ( ) const

inline

Definition at line 209 of file kdTree.h.

◆ pointCount()

template<typename Traits >

IndexType Ponca::KdTreeBase< Traits >::pointCount ( ) const

inline

Definition at line 189 of file kdTree.h.

◆ pointDataFromSample() [1/2]

template<typename Traits >

DataPoint & Ponca::KdTreeBase< Traits >::pointDataFromSample ( IndexType sample_index )

inline

Return the DataPoint associated with the specified sample index.

Note: Convenience function, equivalent to point_data()[pointFromSample(sample_index)]

Definition at line 245 of file kdTree.h.

◆ pointDataFromSample() [2/2]

template<typename Traits >

const DataPoint & Ponca::KdTreeBase< Traits >::pointDataFromSample ( IndexType sample_index ) const

inline

Return the DataPoint associated with the specified sample index.

Note: Convenience function, equivalent to point_data()[pointFromSample(sample_index)]

Definition at line 253 of file kdTree.h.

◆ pointFromSample()

template<typename Traits >

IndexType Ponca::KdTreeBase< Traits >::pointFromSample ( IndexType sample_index ) const

inline

Return the point index associated with the specified sample index.

Definition at line 237 of file kdTree.h.

◆ points() [1/2]

template<typename Traits >

PointContainer & Ponca::KdTreeBase< Traits >::points ( )

inline

Definition at line 199 of file kdTree.h.

◆ points() [2/2]

template<typename Traits >

const PointContainer & Ponca::KdTreeBase< Traits >::points ( ) const

inline

Definition at line 204 of file kdTree.h.

◆ print()

template<typename Traits >

void KdTreeBase::print	(	std::ostream &	os,
		bool	verbose = `false`
	)		const

inline

Definition at line 75 of file kdTree.hpp.

◆ rangeNeighbors() [1/2]

template<typename Traits >

KdTreeRangePointQuery< Traits > Ponca::KdTreeBase< Traits >::rangeNeighbors	(	const VectorType &	point,
		Scalar	r
	)		const

inline

Computes a Query object to iterate over the neighbors that are inside a given radius. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

Parameters

point	Point from where the query is evaluated
r	Radius around where to search the neighbors

Returns: The KdTreeRangePointQuery mutable object to iterate over the search results.

See also: KdTreeRangeQueryBase

Definition at line 367 of file kdTree.h.

◆ rangeNeighbors() [2/2]

template<typename Traits >

KdTreeRangeIndexQuery< Traits > Ponca::KdTreeBase< Traits >::rangeNeighbors	(	IndexType	index,
		Scalar	r
	)		const

inline

Computes a Query object to iterate over the neighbors that are inside a given radius. The returned object can be reset and reused with the () operator (using the same argument types as parameters).

Parameters

index	Index of the point from where the query is evaluated
r	Radius around where to search the neighbors

Returns: The KdTreeRangeIndexQuery mutable object to iterate over the search results.

See also: KdTreeRangeQueryBase

Definition at line 377 of file kdTree.h.

◆ rangeNeighborsIndexQuery()

template<typename Traits >

KdTreeRangeIndexQuery< Traits > Ponca::KdTreeBase< Traits >::rangeNeighborsIndexQuery ( ) const

inline

KdTreeBase::rangeNeighborsQuery.

The returned object can call for a range neighbor search using the operator (), which takes an index as parameter.

Same as KdTreeBase::rangeNeighborsQuery (0, 0)

Returns: The empty KdTreeRangeIndexQuery mutable object to iterate over the search results.

See also: KdTreeRangeQueryBase

Definition at line 405 of file kdTree.h.

◆ rangeNeighborsQuery()

template<typename Traits >

KdTreeRangePointQuery< Traits > Ponca::KdTreeBase< Traits >::rangeNeighborsQuery ( ) const

inline

Convenience function that provides an empty range neighbor Query object.

The returned object can call for a range neighbor search using the operator (), which takes a position as parameter.

Same as KdTreeBase::rangeNeighborsQuery (0, VectorType::Zero())

Returns: The empty KdTreeRangePointQuery mutable object to iterate over the search results.

See also: KdTreeRangeQueryBase

Definition at line 391 of file kdTree.h.

◆ sample_count()

template<typename Traits >

IndexType Ponca::KdTreeBase< Traits >::sample_count ( ) const

inline

Definition at line 184 of file kdTree.h.

◆ samples()

template<typename Traits >

const IndexContainer & Ponca::KdTreeBase< Traits >::samples ( ) const

inline

Definition at line 214 of file kdTree.h.

◆ setMinCellSize()

template<typename Traits >

void Ponca::KdTreeBase< Traits >::setMinCellSize ( LeafSizeType min_cell_size )

inline

Write leaf min size.

Definition at line 228 of file kdTree.h.

◆ valid()

template<typename Traits >

bool KdTreeBase::valid ( ) const

inline

Definition at line 28 of file kdTree.hpp.

Member Data Documentation

◆ m_indices

template<typename Traits >

IndexContainer Ponca::KdTreeBase< Traits >::m_indices

protected

Definition at line 419 of file kdTree.h.

◆ m_leaf_count

template<typename Traits >

NodeIndexType Ponca::KdTreeBase< Traits >::m_leaf_count {0}

protected

Number of leaves in the Kdtree (computed during construction)

Definition at line 422 of file kdTree.h.

◆ m_min_cell_size

template<typename Traits >

LeafSizeType Ponca::KdTreeBase< Traits >::m_min_cell_size {64}

protected

Minimal number of points per leaf.

Definition at line 421 of file kdTree.h.

◆ m_nodes

template<typename Traits >

NodeContainer Ponca::KdTreeBase< Traits >::m_nodes

protected

Definition at line 418 of file kdTree.h.

◆ m_points

template<typename Traits >

PointContainer Ponca::KdTreeBase< Traits >::m_points

protected

Definition at line 417 of file kdTree.h.

◆ MAX_DEPTH

template<typename Traits >

constexpr int Ponca::KdTreeBase< Traits >::MAX_DEPTH = Traits::MAX_DEPTH

staticconstexpr

The maximum depth of the kd-tree.

Definition at line 125 of file kdTree.h.

◆ MAX_NODE_COUNT

template<typename Traits >

constexpr std::size_t Ponca::KdTreeBase< Traits >::MAX_NODE_COUNT = NodeType::MAX_COUNT

staticconstexpr

The maximum number of nodes that the kd-tree can have.

Definition at line 120 of file kdTree.h.

◆ MAX_POINT_COUNT

template<typename Traits >

constexpr std::size_t Ponca::KdTreeBase< Traits >::MAX_POINT_COUNT = std::size_t(2) << sizeof(IndexType)*8

staticconstexpr

The maximum number of points that can be stored in the kd-tree.

Definition at line 122 of file kdTree.h.

◆ SUPPORTS_SUBSAMPLING

template<typename Traits >

constexpr bool Ponca::KdTreeBase< Traits >::SUPPORTS_SUBSAMPLING = false

staticconstexpr

Definition at line 127 of file kdTree.h.

Classes

Public Types

Public Member Functions

Static Public Attributes

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ AabbType

◆ DataPoint

◆ IndexContainer

◆ IndexType

◆ LeafSizeType

◆ NodeContainer

◆ NodeIndexType

◆ NodeType

◆ PointContainer

◆ Scalar

◆ VectorType

Member Function Documentation

◆ build() [1/3]

◆ build() [2/3]

◆ build() [3/3]

◆ buildWithSampling() [1/3]

◆ buildWithSampling() [2/3]

◆ buildWithSampling() [3/3]

◆ clear()

◆ kNearestNeighbors() [1/2]

◆ kNearestNeighbors() [2/2]

◆ kNearestNeighborsIndexQuery()

◆ kNearestNeighborsQuery()

◆ leaf_count()

◆ minCellSize()

◆ nearestNeighbor() [1/2]

◆ nearestNeighbor() [2/2]

◆ nearestNeighborIndexQuery()

◆ nearestNeighborQuery()

◆ node_count()

◆ nodes()

◆ pointCount()

◆ pointDataFromSample() [1/2]

◆ pointDataFromSample() [2/2]

◆ pointFromSample()

◆ points() [1/2]

◆ points() [2/2]

◆ print()

◆ rangeNeighbors() [1/2]

◆ rangeNeighbors() [2/2]

◆ rangeNeighborsIndexQuery()

◆ rangeNeighborsQuery()

◆ sample_count()

◆ samples()

◆ setMinCellSize()

◆ valid()

Member Data Documentation

◆ m_indices

◆ m_leaf_count

◆ m_min_cell_size

◆ m_nodes

◆ m_points

◆ MAX_DEPTH

◆ MAX_NODE_COUNT

◆ MAX_POINT_COUNT

◆ SUPPORTS_SUBSAMPLING