#include <nanoflann.hpp>

Inheritance diagram for nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >:

Collaboration diagram for nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >:

Public Types
using	Base = typename nanoflann::KDTreeBaseClass< nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >, Distance, DatasetAdaptor, DIM, IndexType >

using	Offset = typename Base::Offset

using	Size = typename Base::Size

using	Dimension = typename Base::Dimension

using	ElementType = typename Base::ElementType

using	DistanceType = typename Base::DistanceType

using	Node = typename Base::Node

using	NodePtr = Node *

using	Interval = typename Base::Interval

using	BoundingBox = typename Base::BoundingBox

using	distance_vector_t = typename Base::distance_vector_t

Public Types inherited from nanoflann::KDTreeBaseClass< Derived, Distance, DatasetAdaptor, DIM, IndexType >
using	ElementType = typename Distance::ElementType

using	DistanceType = typename Distance::DistanceType

using	Offset = typename decltype(vAcc_)::size_type

using	Size = typename decltype(vAcc_)::size_type

using	Dimension = int32_t

using	NodePtr = Node *

using	NodeConstPtr = const Node *

using	BoundingBox = typename array_or_vector< DIM, Interval >::type

using	distance_vector_t = typename array_or_vector< DIM, DistanceType >::type

Public Member Functions
	KDTreeSingleIndexAdaptor (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)=delete

template<class... Args>
	KDTreeSingleIndexAdaptor (const Dimension dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams &params, Args &&... args)

	KDTreeSingleIndexAdaptor (const Dimension dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams &params={})

void	buildIndex ()

void	init_vind ()

void	computeBoundingBox (BoundingBox &bbox)

template<class RESULTSET >
bool	searchLevel (RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindist, distance_vector_t &dists, const float epsError) const

void	saveIndex (std::ostream &stream) const

void	loadIndex (std::istream &stream)

Query methods
template<typename RESULTSET >
bool	findNeighbors (RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const

Size	knnSearch (const ElementType query_point, const Size num_closest, IndexType out_indices, DistanceType *out_distances) const

Size	radiusSearch (const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const

template<class SEARCH_CALLBACK >
Size	radiusSearchCustomCallback (const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const

Size	rknnSearch (const ElementType query_point, const Size num_closest, IndexType out_indices, DistanceType *out_distances, const DistanceType &radius) const

Public Member Functions inherited from nanoflann::KDTreeBaseClass< Derived, Distance, DatasetAdaptor, DIM, IndexType >
void	freeIndex (Derived &obj)

Size	size (const Derived &obj) const

Size	veclen (const Derived &obj)

ElementType	dataset_get (const Derived &obj, IndexType element, Dimension component) const
	Helper accessor to the dataset points:

Size	usedMemory (Derived &obj)

void	computeMinMax (const Derived &obj, Offset ind, Size count, Dimension element, ElementType &min_elem, ElementType &max_elem)

NodePtr	divideTree (Derived &obj, const Offset left, const Offset right, BoundingBox &bbox)

NodePtr	divideTreeConcurrent (Derived &obj, const Offset left, const Offset right, BoundingBox &bbox, std::atomic< unsigned int > &thread_count, std::mutex &mutex)

void	middleSplit_ (const Derived &obj, const Offset ind, const Size count, Offset &index, Dimension &cutfeat, DistanceType &cutval, const BoundingBox &bbox)

void	planeSplit (const Derived &obj, const Offset ind, const Size count, const Dimension cutfeat, const DistanceType &cutval, Offset &lim1, Offset &lim2)

DistanceType	computeInitialDistances (const Derived &obj, const ElementType *vec, distance_vector_t &dists) const

void	saveIndex (const Derived &obj, std::ostream &stream) const

void	loadIndex (Derived &obj, std::istream &stream)

Public Attributes
const DatasetAdaptor &	dataset_

const KDTreeSingleIndexAdaptorParams	indexParams

Distance	distance_

Public Attributes inherited from nanoflann::KDTreeBaseClass< Derived, Distance, DatasetAdaptor, DIM, IndexType >
std::vector< IndexType >	vAcc_

NodePtr	root_node_ = nullptr

Size	leaf_max_size_ = 0

Size	n_thread_build_ = 1
	Number of thread for concurrent tree build.

Size	size_ = 0
	Number of current points in the dataset.

Size	size_at_index_build_ = 0
	Number of points in the dataset when the index was built.

Dimension	dim_ = 0
	Dimensionality of each data point.

BoundingBox	root_bbox_

PooledAllocator	pool_

Additional Inherited Members
Static Public Member Functions inherited from nanoflann::KDTreeBaseClass< Derived, Distance, DatasetAdaptor, DIM, IndexType >
static void	save_tree (const Derived &obj, std::ostream &stream, const NodeConstPtr tree)

static void	load_tree (Derived &obj, std::istream &stream, NodePtr &tree)

Detailed Description

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename IndexType = uint32_t>
class nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >

kd-tree static index

Contains the k-d trees and other information for indexing a set of points for nearest-neighbor matching.

The class "DatasetAdaptor" must provide the following interface (can be non-virtual, inlined methods):

  // Must return the number of data poins
  size_t kdtree_get_point_count() const { ... }
 
 
  // Must return the dim'th component of the idx'th point in the class:
  T kdtree_get_pt(const size_t idx, const size_t dim) const { ... }
 
  // Optional bounding-box computation: return false to default to a standard
bbox computation loop.
  //   Return true if the BBOX was already computed by the class and returned
in "bb" so it can be avoided to redo it again.
  //   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3
for point clouds) template <class BBOX> bool kdtree_get_bbox(BBOX &bb) const
  {
     bb[0].low = ...; bb[0].high = ...;  // 0th dimension limits
     bb[1].low = ...; bb[1].high = ...;  // 1st dimension limits
     ...
     return true;
  }

Template Parameters

DatasetAdaptor	The user-provided adaptor, which must be ensured to have a lifetime equal or longer than the instance of this class.
Distance	The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
DIM	Dimensionality of data points (e.g. 3 for 3D points)
IndexType	Will be typically size_t or int

Member Typedef Documentation

◆ Base

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Base = typename nanoflann::KDTreeBaseClass<nanoflann::KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>, Distance, DatasetAdaptor, DIM, IndexType>

◆ BoundingBox

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::BoundingBox = typename Base::BoundingBox

Define "BoundingBox" as a fixed-size or variable-size container depending on "DIM"

◆ Dimension

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Dimension = typename Base::Dimension

◆ distance_vector_t

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::distance_vector_t = typename Base::distance_vector_t

Define "distance_vector_t" as a fixed-size or variable-size container depending on "DIM"

◆ DistanceType

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::DistanceType = typename Base::DistanceType

◆ ElementType

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::ElementType = typename Base::ElementType

◆ Interval

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Interval = typename Base::Interval

◆ Node

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node = typename Base::Node

◆ NodePtr

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::NodePtr = Node*

◆ Offset

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Offset = typename Base::Offset

◆ Size

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Size = typename Base::Size

Constructor & Destructor Documentation

◆ KDTreeSingleIndexAdaptor() [1/3]

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexAdaptor ( const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > & )

explicitdelete

Deleted copy constructor

◆ KDTreeSingleIndexAdaptor() [2/3]

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

template<class... Args>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexAdaptor	(	const Dimension	dimensionality,
		const DatasetAdaptor &	inputData,
		const KDTreeSingleIndexAdaptorParams &	params,
		Args &&...	args
	)

inlineexplicit

KDTree constructor

Refer to docs in README.md or online in https://github.com/jlblancoc/nanoflann

The KD-Tree point dimension (the length of each point in the datase, e.g. 3 for 3D points) is determined by means of:

The DIM template parameter if >0 (highest priority)
Otherwise, the dimensionality parameter of this constructor.

Parameters

inputData	Dataset with the input features. Its lifetime must be equal or longer than that of the instance of this class.
params	Basically, the maximum leaf node size

Note that there is a variable number of optional additional parameters which will be forwarded to the metric class constructor. Refer to example examples/pointcloud_custom_metric.cpp for a use case.

◆ KDTreeSingleIndexAdaptor() [3/3]

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexAdaptor	(	const Dimension	dimensionality,
		const DatasetAdaptor &	inputData,
		const KDTreeSingleIndexAdaptorParams &	params = `{}`
	)

inlineexplicit

Member Function Documentation

◆ buildIndex()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::buildIndex ( )

inline

Builds the index

◆ computeBoundingBox()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::computeBoundingBox ( BoundingBox & bbox )

inline

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◆ findNeighbors()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

template<typename RESULTSET >

bool nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::findNeighbors	(	RESULTSET &	result,
		const ElementType *	vec,
		const SearchParameters &	searchParams = `{}`
	)		const

inline

Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored inside the result object.

Params: result = the result object in which the indices of the nearest-neighbors are stored vec = the vector for which to search the nearest neighbors

Template Parameters

RESULTSET Should be any ResultSet<DistanceType>

Returns: True if the requested neighbors could be found.

See also: knnSearch, radiusSearch

Note: If L2 norms are used, all returned distances are actually squared distances.

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◆ init_vind()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::init_vind ( )

inline

Make sure the auxiliary list vind has the same size than the current dataset, and re-generate if size has changed.

◆ knnSearch()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

Size nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::knnSearch	(	const ElementType *	query_point,
		const Size	num_closest,
		IndexType *	out_indices,
		DistanceType *	out_distances
	)		const

inline

Find the "num_closest" nearest neighbors to the query_point[0:dim-1]. Their indices and distances are stored in the provided pointers to array/vector.

See also: radiusSearch, findNeighbors

Returns: Number N of valid points in the result set.

Note: If L2 norms are used, all returned distances are actually squared distances.; Only the first N entries in out_indices and out_distances will be valid. Return is less than num_closest only if the number of elements in the tree is less than num_closest.

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◆ loadIndex()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::loadIndex ( std::istream & stream )

inline

Loads a previous index from a binary file. IMPORTANT NOTE: The set of data points is NOT stored in the file, so the index object must be constructed associated to the same source of data points used while building the index. See the example: examples/saveload_example.cpp

See also: loadIndex

◆ radiusSearch()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

Size nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::radiusSearch	(	const ElementType *	query_point,
		const DistanceType &	radius,
		std::vector< ResultItem< IndexType, DistanceType > > &	IndicesDists,
		const SearchParameters &	searchParams = `{}`
	)		const

inline

Find all the neighbors to query_point[0:dim-1] within a maximum radius. The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance. Previous contents of IndicesDists are cleared.

If searchParams.sorted==true, the output list is sorted by ascending distances.

For a better performance, it is advisable TODO(lf)a .reserve() on the vector if you have any wild guess about the number of expected matches.

See also: knnSearch, findNeighbors, radiusSearchCustomCallback

Returns: The number of points within the given radius (i.e. indices.size() or dists.size() )

Note: If L2 norms are used, search radius and all returned distances are actually squared distances.

◆ radiusSearchCustomCallback()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

template<class SEARCH_CALLBACK >

Size nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::radiusSearchCustomCallback	(	const ElementType *	query_point,
		SEARCH_CALLBACK &	resultSet,
		const SearchParameters &	searchParams = `{}`
	)		const

inline

Just like radiusSearch() but with a custom callback class for each point found in the radius of the query. See the source of RadiusResultSet<> as a start point for your own classes.

See also: radiusSearch

◆ rknnSearch()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

Size nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::rknnSearch	(	const ElementType *	query_point,
		const Size	num_closest,
		IndexType *	out_indices,
		DistanceType *	out_distances,
		const DistanceType &	radius
	)		const

inline

Find the first N neighbors to query_point[0:dim-1] within a maximum radius. The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance. Previous contents of IndicesDists are cleared.

See also: radiusSearch, findNeighbors

Returns: Number N of valid points in the result set.

Note: If L2 norms are used, all returned distances are actually squared distances.; Only the first N entries in out_indices and out_distances will be valid. Return is less than num_closest only if the number of elements in the tree is less than num_closest.

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◆ saveIndex()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::saveIndex ( std::ostream & stream ) const

inline

Stores the index in a binary file. IMPORTANT NOTE: The set of data points is NOT stored in the file, so when loading the index object it must be constructed associated to the same source of data points used while building it. See the example: examples/saveload_example.cpp

See also: loadIndex

◆ searchLevel()

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

template<class RESULTSET >

bool nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::searchLevel	(	RESULTSET &	result_set,
		const ElementType *	vec,
		const NodePtr	node,
		DistanceType	mindist,
		distance_vector_t &	dists,
		const float	epsError
	)		const

inline

Performs an exact search in the tree starting from a node.

Template Parameters

RESULTSET Should be any ResultSet<DistanceType>

Returns: true if the search should be continued, false if the results are sufficient

Member Data Documentation

◆ dataset_

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

const DatasetAdaptor& nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::dataset_

The data source used by this index

◆ distance_

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

Distance nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::distance_

◆ indexParams

template<typename Distance , class DatasetAdaptor , int32_t DIM = -1, typename IndexType = uint32_t>

const KDTreeSingleIndexAdaptorParams nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::indexParams

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

src/lib/patchGeneration/nanoflann.hpp

Public Types

Public Member Functions

Public Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

◆ BoundingBox

◆ Dimension

◆ distance_vector_t

◆ DistanceType

◆ ElementType

◆ Interval

◆ Node

◆ NodePtr

◆ Offset

◆ Size

Constructor & Destructor Documentation

◆ KDTreeSingleIndexAdaptor() [1/3]

◆ KDTreeSingleIndexAdaptor() [2/3]

◆ KDTreeSingleIndexAdaptor() [3/3]

Member Function Documentation

◆ buildIndex()

◆ computeBoundingBox()

◆ findNeighbors()

◆ init_vind()

◆ knnSearch()

◆ loadIndex()

◆ radiusSearch()

◆ radiusSearchCustomCallback()

◆ rknnSearch()

◆ saveIndex()

◆ searchLevel()

Member Data Documentation

◆ dataset_

◆ distance_

◆ indexParams