open3d::core::nns::impl Namespace Reference

Functions
template<class T , class TIndex , class OUTPUT_ALLOCATOR >
void	FixedRadiusSearchCPU (int64_t *query_neighbors_row_splits, const size_t num_points, const T *const points, const size_t num_queries, const T *const queries, const T radius, const size_t points_row_splits_size, const int64_t *const points_row_splits, const size_t queries_row_splits_size, const int64_t *const queries_row_splits, const uint32_t *const hash_table_splits, const size_t hash_table_cell_splits_size, const uint32_t *const hash_table_cell_splits, const uint32_t *const hash_table_index, const Metric metric, const bool ignore_query_point, const bool return_distances, OUTPUT_ALLOCATOR &output_allocator)
template<class T , class TIndex >
std::unique_ptr< NanoFlannIndexHolderBase >	BuildKdTree (size_t num_points, const T *const points, size_t dimension, const Metric metric)
template<class T , class TIndex , class OUTPUT_ALLOCATOR >
void	KnnSearchCPU (NanoFlannIndexHolderBase *holder, int64_t *query_neighbors_row_splits, size_t num_points, const T *const points, size_t num_queries, const T *const queries, const size_t dimension, int knn, const Metric metric, bool ignore_query_point, bool return_distances, OUTPUT_ALLOCATOR &output_allocator)
template<class T , class TIndex , class OUTPUT_ALLOCATOR >
void	RadiusSearchCPU (NanoFlannIndexHolderBase *holder, int64_t *query_neighbors_row_splits, size_t num_points, const T *const points, size_t num_queries, const T *const queries, const size_t dimension, const T *const radii, const Metric metric, bool ignore_query_point, bool return_distances, bool normalize_distances, bool sort, OUTPUT_ALLOCATOR &output_allocator)
template<class T , class TIndex , class OUTPUT_ALLOCATOR >
void	HybridSearchCPU (NanoFlannIndexHolderBase *holder, size_t num_points, const T *const points, size_t num_queries, const T *const queries, const size_t dimension, const T radius, const int max_knn, const Metric metric, bool ignore_query_point, bool return_distances, OUTPUT_ALLOCATOR &output_allocator)

Function Documentation

BuildKdTree()

template<class T , class TIndex >

std::unique_ptr<NanoFlannIndexHolderBase> open3d::core::nns::impl::BuildKdTree	(	size_t	num_points,
		const T *const	points,
		size_t	dimension,
		const Metric	metric
	)

Build KD Tree. This function build a KDTree for given dataset points.

Template Parameters

T	Floating-point data type for the point positions.

Parameters

num_points	The number of points.
points	Array with the point positions.
dimension	The dimension of points.
metric	Onf of L1, L2. Defines the distance metric for the search

FixedRadiusSearchCPU()

template<class T , class TIndex , class OUTPUT_ALLOCATOR >

void open3d::core::nns::impl::FixedRadiusSearchCPU	(	int64_t *	query_neighbors_row_splits,
		const size_t	num_points,
		const T *const	points,
		const size_t	num_queries,
		const T *const	queries,
		const T	radius,
		const size_t	points_row_splits_size,
		const int64_t *const	points_row_splits,
		const size_t	queries_row_splits_size,
		const int64_t *const	queries_row_splits,
		const uint32_t *const	hash_table_splits,
		const size_t	hash_table_cell_splits_size,
		const uint32_t *const	hash_table_cell_splits,
		const uint32_t *const	hash_table_index,
		const Metric	metric,
		const bool	ignore_query_point,
		const bool	return_distances,
		OUTPUT_ALLOCATOR &	output_allocator
	)

Fixed radius search. This function computes a list of neighbor indices for each query point. The lists are stored linearly and an exclusive prefix sum defines the start and end of list in the array. In addition the function optionally can return the distances for each neighbor in the same format as the indices to the neighbors.

Template Parameters

T	Floating-point data type for the point positions.
OUTPUT_ALLOCATOR	Type of the output_allocator. See output_allocator for more information.

Parameters

query_neighbors_row_splits	This is the output pointer for the prefix sum. The length of this array is num_queries + 1.
num_points	The number of points.
points	Array with the 3D point positions. This must be the array that was used for building the spatial hash table.
num_queries	The number of query points.
queries	Array with the 3D query positions. This may be the same array as points.
radius	The search radius.
points_row_splits_size	The size of the points_row_splits array. The size of the array is batch_size+1.
points_row_splits	Defines the start and end of the points in each batch item. The size of the array is batch_size+1. If there is only 1 batch item then this array is [0, num_points]
queries_row_splits_size	The size of the queries_row_splits array. The size of the array is batch_size+1.
queries_row_splits	Defines the start and end of the queries in each batch item. The size of the array is batch_size+1. If there is only 1 batch item then this array is [0, num_queries]
hash_table_splits	Array defining the start and end the hash table for each batch item. This is [0, number of cells] if there is only 1 batch item or [0, hash_table_cell_splits_size-1] which is the same.
hash_table_cell_splits_size	This is the length of the hash_table_cell_splits array.
hash_table_cell_splits	This is an output of the function BuildSpatialHashTableCPU. The row splits array describing the start and end of each cell.
hash_table_index	This is an output of the function BuildSpatialHashTableCPU. This is array storing the values of the hash table, which are the indices to the points. The size of the array must be equal to the number of points.
metric	One of L1, L2, Linf. Defines the distance metric for the search.
ignore_query_point	If true then points with the same position as the query point will be ignored.
return_distances	If true then this function will return the distances for each neighbor to its query point in the same format as the indices. Note that for the L2 metric the squared distances will be returned!!
output_allocator	An object that implements functions for allocating the output arrays. The object must implement functions AllocIndices(int64_t** ptr, size_t size) and AllocDistances(T** ptr, size_t size). Both functions should allocate memory and return a pointer to that memory in ptr. Argument size specifies the size of the array as the number of elements. Both functions must accept the argument size==0. In this case ptr does not need to be set.

HybridSearchCPU()

template<class T , class TIndex , class OUTPUT_ALLOCATOR >

void open3d::core::nns::impl::HybridSearchCPU	(	NanoFlannIndexHolderBase *	holder,
		size_t	num_points,
		const T *const	points,
		size_t	num_queries,
		const T *const	queries,
		const size_t	dimension,
		const T	radius,
		const int	max_knn,
		const Metric	metric,
		bool	ignore_query_point,
		bool	return_distances,
		OUTPUT_ALLOCATOR &	output_allocator
	)

Hybrid search. This function computes a list of neighbor indices for each query point. The lists are stored linearly and an exclusive prefix sum defines the start and end of each list in the array. In addition the function optionally can return the distances for each neighbor in the same format as the indices to the neighbors.

Template Parameters

T	Floating-point data type for the point positions.
OUTPUT_ALLOCATOR	Type of the output_allocator. See output_allocator for more information.

Parameters

holder	The pointer that point to NanFlannIndexHolder that is built with BuildKdTree function.
num_points	The number of points.
points	Array with the point positions. This may be the same array as queries.
num_queries	The number of query points.
queries	Array with the query positions. This may be the same array as points.
dimension	The dimension of points and queries.
radius	The radius value that defines the neighbors region.
max_knn	The maximum number of neighbors to search.
metric	One of L1, L2. Defines the distance metric for the search.
ignore_query_point	If true then points with the same position as the query point will be ignored.
return_distances	If true then this function will return the distances for each neighbor to its query point in the same format as the indices. Note that for the L2 metric the squared distances will be returned!!
output_allocator	An object that implements functions for allocating the output arrays. The object must implement functions AllocIndices(int32_t** ptr, size_t size) and AllocDistances(T** ptr, size_t size). Both functions should allocate memory and return a pointer to that memory in ptr. Argument size specifies the size of the array as the number of elements. Both functions must accept the argument size==0. In this case ptr does not need to be set.

KnnSearchCPU()

template<class T , class TIndex , class OUTPUT_ALLOCATOR >

void open3d::core::nns::impl::KnnSearchCPU	(	NanoFlannIndexHolderBase *	holder,
		int64_t *	query_neighbors_row_splits,
		size_t	num_points,
		const T *const	points,
		size_t	num_queries,
		const T *const	queries,
		const size_t	dimension,
		int	knn,
		const Metric	metric,
		bool	ignore_query_point,
		bool	return_distances,
		OUTPUT_ALLOCATOR &	output_allocator
	)

KNN search. This function computes a list of neighbor indices for each query point. The lists are stored linearly and an exclusive prefix sum defines the start and end of each list in the array. In addition the function optionally can return the distances for each neighbor in the same format as the indices to the neighbors.

Template Parameters

T	Floating-point data type for the point positions.
OUTPUT_ALLOCATOR	Type of the output_allocator. See output_allocator for more information.

Parameters

holder	The pointer that point to NanFlannIndexHolder that is built with BuildKdTree function.
query_neighbors_row_splits	This is the output pointer for the prefix sum. The length of this array is num_queries + 1.
num_points	The number of points.
points	Array with the point positions. This may be the same array as queries.
num_queries	The number of query points.
queries	Array with the query positions. This may be the same array as points.
dimension	The dimension of points and queries.
knn	The number of neighbors to search.
metric	One of L1, L2. Defines the distance metric for the search.
ignore_query_point	If true then points with the same position as the query point will be ignored.
return_distances	If true then this function will return the distances for each neighbor to its query point in the same format as the indices. Note that for the L2 metric the squared distances will be returned!!
output_allocator	An object that implements functions for allocating the output arrays. The object must implement functions AllocIndices(int32_t** ptr, size_t size) and AllocDistances(T** ptr, size_t size). Both functions should allocate memory and return a pointer to that memory in ptr. Argument size specifies the size of the array as the number of elements. Both functions must accept the argument size==0. In this case ptr does not need to be set.

RadiusSearchCPU()

template<class T , class TIndex , class OUTPUT_ALLOCATOR >

void open3d::core::nns::impl::RadiusSearchCPU	(	NanoFlannIndexHolderBase *	holder,
		int64_t *	query_neighbors_row_splits,
		size_t	num_points,
		const T *const	points,
		size_t	num_queries,
		const T *const	queries,
		const size_t	dimension,
		const T *const	radii,
		const Metric	metric,
		bool	ignore_query_point,
		bool	return_distances,
		bool	normalize_distances,
		bool	sort,
		OUTPUT_ALLOCATOR &	output_allocator
	)

Radius search. This function computes a list of neighbor indices for each query point. The lists are stored linearly and an exclusive prefix sum defines the start and end of each list in the array. In addition the function optionally can return the distances for each neighbor in the same format as the indices to the neighbors.

Template Parameters

T	Floating-point data type for the point positions.
OUTPUT_ALLOCATOR	Type of the output_allocator. See output_allocator for more information.

Parameters

holder	The pointer that point to NanFlannIndexHolder that is built with BuildKdTree function.
query_neighbors_row_splits	This is the output pointer for the prefix sum. The length of this array is num_queries + 1.
num_points	The number of points.
points	Array with the point positions. This may be the same array as queries.
num_queries	The number of query points.
queries	Array with the query positions. This may be the same array as points.
dimension	The dimension of points and queries.
radii	A vector of search radii with length num_queries.
metric	One of L1, L2. Defines the distance metric for the search.
ignore_query_point	If true then points with the same position as the query point will be ignored.
return_distances	If true then this function will return the distances for each neighbor to its query point in the same format as the indices. Note that for the L2 metric the squared distances will be returned!!
normalize_distances	If true then the returned distances are normalized in the range [0,1]. Note that for L2 the normalized distance is squared.
sort	If true then sort the resulting indices and distances in ascending order of distances.
output_allocator	An object that implements functions for allocating the output arrays. The object must implement functions AllocIndices(int32_t** ptr, size_t size) and AllocDistances(T** ptr, size_t size). Both functions should allocate memory and return a pointer to that memory in ptr. Argument size specifies the size of the array as the number of elements. Both functions must accept the argument size==0. In this case ptr does not need to be set.