KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementation. More...

#include <nabo_private.h>

Inheritance diagram for Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap, CloudType >:

Classes
struct	BucketEntry
	entry in a bucket More...

struct	Node
	search node More...

Public Types
typedef NearestNeighbourSearch< T, CloudType >::Vector	Vector

typedef NearestNeighbourSearch< T, CloudType >::Matrix	Matrix

typedef NearestNeighbourSearch< T, CloudType >::Index	Index

typedef NearestNeighbourSearch< T, CloudType >::IndexVector	IndexVector

typedef NearestNeighbourSearch< T, CloudType >::IndexMatrix	IndexMatrix

Public Types inherited from Nabo::NearestNeighbourSearch< T, CloudType >
enum	SearchType
	type of search

enum	CreationOptionFlags
	creation option

enum	SearchOptionFlags
	search option

typedef Eigen::Matrix< T, Eigen::Dynamic, 1 >	Vector
	an Eigen vector of type T, to hold the coordinates of a point

typedef Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic >	Matrix
	a column-major Eigen matrix in which each column is a point; this matrix has dim rows

typedef CloudType	CloudType
	a column-major Eigen matrix in which each column is a point; this matrix has dim rows

typedef int	Index
	an index to a Vector or a Matrix, for refering to data points

typedef Eigen::Matrix< Index, Eigen::Dynamic, 1 >	IndexVector
	a vector of indices to data points

typedef Eigen::Matrix< Index, Eigen::Dynamic, Eigen::Dynamic >	IndexMatrix
	a matrix of indices to data points

Protected Types
typedef std::vector< Index >	BuildPoints
	indices of points during kd-tree construction

typedef BuildPoints::iterator	BuildPointsIt
	iterator to indices of points during kd-tree construction

typedef BuildPoints::const_iterator	BuildPointsCstIt
	const-iterator to indices of points during kd-tree construction

typedef std::vector< Node >	Nodes
	dense vector of search nodes, provides better memory performances than many small objects

typedef std::vector< BucketEntry >	Buckets
	bucket data

Protected Member Functions
uint32_t	createDimChildBucketSize (const uint32_t dim, const uint32_t childIndex) const
	create the compound index containing the dimension and the index of the child or the bucket size

uint32_t	getDim (const uint32_t dimChildBucketSize) const
	get the dimension out of the compound index

uint32_t	getChildBucketSize (const uint32_t dimChildBucketSize) const
	get the child index or the bucket size out of the coumpount index

Protected Member Functions inherited from Nabo::NearestNeighbourSearch< T, CloudType >
	NearestNeighbourSearch (const CloudType &cloud, const Index dim, const unsigned creationOptionFlags)
	constructor

void	checkSizesKnn (const Matrix &query, const IndexMatrix &indices, const Matrix &dists2, const Index k, const unsigned optionFlags, const Vector *maxRadii=0) const
	Make sure that the output matrices have the right sizes. Throw an exception otherwise. More...

Protected Attributes
const unsigned	bucketSize
	size of bucket

const uint32_t	dimBitCount
	number of bits required to store dimension index + number of dimensions

const uint32_t	dimMask
	mask to access dim

Nodes	nodes
	search nodes

Buckets	buckets
	buckets

std::pair< T, T >	getBounds (const BuildPointsIt first, const BuildPointsIt last, const unsigned dim)
	return the bounds of points from [first..last[ on dimension dim

unsigned	buildNodes (const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues)
	construct nodes for points [first..last[ inside the hyperrectangle [minValues..maxValues]

unsigned long	onePointKnn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, int i, Heap &heap, std::vector< T > &off, const T maxError, const T maxRadius2, const bool allowSelfMatch, const bool collectStatistics, const bool sortResults) const
	search one point, call recurseKnn with the correct template parameters More...

template<bool allowSelfMatch, bool collectStatistics>
unsigned long	recurseKnn (const T *query, const unsigned n, T rd, Heap &heap, std::vector< T > &off, const T maxError, const T maxRadius2) const
	recursive search, strongly inspired by ANN and [Arya & Mount, Algorithms for fast vector quantization, 1993] More...

	KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt (const CloudType &cloud, const Index dim, const unsigned creationOptionFlags, const Parameters &additionalParameters)
	constructor, calls NearestNeighbourSearch<T>(cloud)

virtual unsigned long	knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const

virtual unsigned long	knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Vector &maxRadii, const Index k=1, const T epsilon=0, const unsigned optionFlags=0) const

Additional Inherited Members
Public Member Functions inherited from Nabo::NearestNeighbourSearch< T, CloudType >
unsigned long	knn (const Vector &query, IndexVector &indices, Vector &dists2, const Index k=1, const T epsilon=0, const unsigned optionFlags=0, const T maxRadius=std::numeric_limits< T >::infinity()) const
	Find the k nearest neighbours of query. More...

virtual unsigned long	knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Index k=1, const T epsilon=0, const unsigned optionFlags=0, const T maxRadius=std::numeric_limits< T >::infinity()) const=0
	Find the k nearest neighbours for each point of query. More...

virtual unsigned long	knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Vector &maxRadii, const Index k=1, const T epsilon=0, const unsigned optionFlags=0) const=0
	Find the k nearest neighbours for each point of query. More...

virtual	~NearestNeighbourSearch ()
	virtual destructor

Static Public Member Functions inherited from Nabo::NearestNeighbourSearch< T, CloudType >
static NearestNeighbourSearch *	create (const CloudType &cloud, const Index dim=std::numeric_limits< Index >::max(), const SearchType preferedType=KDTREE_LINEAR_HEAP, const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
	Create a nearest-neighbour search. More...

static NearestNeighbourSearch *	create (const WrongMatrixType &cloud, const Index dim=std::numeric_limits< Index >::max(), const SearchType preferedType=KDTREE_LINEAR_HEAP, const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
	Prevent creation of trees with the wrong matrix type. Currently only dynamic size matrices are supported.

static NearestNeighbourSearch *	createBruteForce (const CloudType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0)
	Create a nearest-neighbour search, using brute-force search, useful for comparison only. More...

static NearestNeighbourSearch *	createBruteForce (const WrongMatrixType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0)
	Prevent creation of trees with the wrong matrix type. Currently only dynamic size matrices are supported.

static NearestNeighbourSearch *	createKDTreeLinearHeap (const CloudType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
	Create a nearest-neighbour search, using a kd-tree with linear heap, good for small k (~up to 30) More...

static NearestNeighbourSearch *	createKDTreeLinearHeap (const WrongMatrixType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
	Prevent creation of trees with the wrong matrix type. Currently only dynamic size matrices are supported.

static NearestNeighbourSearch *	createKDTreeTreeHeap (const CloudType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
	Create a nearest-neighbour search, using a kd-tree with tree heap, good for large k (~from 30) More...

static NearestNeighbourSearch *	createKDTreeTreeHeap (const WrongMatrixType &, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
	Prevent creation of trees with the wrong matrix type. Currently only dynamic size matrices are supported.

Public Attributes inherited from Nabo::NearestNeighbourSearch< T, CloudType >
const CloudType &	cloud
	the reference to the data-point cloud, which must remain valid during the lifetime of the NearestNeighbourSearch object

const Index	dim
	the dimensionality of the data-point cloud

const unsigned	creationOptionFlags
	creation options

const Vector	minBound
	the low bound of the search space (axis-aligned bounding box)

const Vector	maxBound
	the high bound of the search space (axis-aligned bounding box)

Static Public Attributes inherited from Nabo::NearestNeighbourSearch< T, CloudType >
static constexpr Index	InvalidIndex
	the invalid index

static constexpr T	InvalidValue
	the invalid value

Detailed Description

template<typename T, typename Heap, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
struct Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap, CloudType >

KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementation.

Member Function Documentation

◆ onePointKnn()

template<typename T , typename Heap , typename CloudType >

unsigned long Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap, CloudType >::onePointKnn	(	const Matrix &	query,
		IndexMatrix &	indices,
		Matrix &	dists2,
		int	i,
		Heap &	heap,
		std::vector< T > &	off,
		const T	maxError,
		const T	maxRadius2,
		const bool	allowSelfMatch,
		const bool	collectStatistics,
		const bool	sortResults
	)		const

protected

search one point, call recurseKnn with the correct template parameters

Parameters

query	pointer to query coordinates
indices	indices of nearest neighbours, must be of size k x query.cols()
dists2	squared distances to nearest neighbours, must be of size k x query.cols()
i	index of point to search
heap	reference to heap
off	reference to array of offsets
maxError	error factor (1 + epsilon)
maxRadius2	square of maximum radius
allowSelfMatch	whether to allow self match
collectStatistics	whether to collect statistics
sortResults	wether to sort results

◆ recurseKnn()

template<typename T , typename Heap , typename CloudType >

template<bool allowSelfMatch, bool collectStatistics>

unsigned long Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap, CloudType >::recurseKnn	(	const T *	query,
		const unsigned	n,
		T	rd,
		Heap &	heap,
		std::vector< T > &	off,
		const T	maxError,
		const T	maxRadius2
	)		const

protected

recursive search, strongly inspired by ANN and [Arya & Mount, Algorithms for fast vector quantization, 1993]

Parameters

query	pointer to query coordinates
n	index of node to visit
rd	squared dist to this rect
heap	reference to heap
off	reference to array of offsets
maxError	error factor (1 + epsilon)
maxRadius2	square of maximum radius

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

Classes

Public Types

Protected Types

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

template<typename T, typename Heap, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>> struct Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap, CloudType >

Member Function Documentation

◆ onePointKnn()

◆ recurseKnn()

template<typename T, typename Heap, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
struct Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap, CloudType >