Template Class KDTreeSingleIndexDynamicAdaptor

Defined in File nanoflann.hpp

Class Documentation

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

kd-tree dynaimic index

class to create multiple static index and merge their results to behave as single dynamic index as proposed in Logarithmic Approach.

Example of usage: examples/dynamic_pointcloud_example.cpp

Template Parameters:

DatasetAdaptor – The user-provided adaptor (see comments above).
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

Public Types

using ElementType = typename Distance::ElementType

using DistanceType = typename Distance::DistanceType

using Offset = typename KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>::Offset

using Size = typename KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>::Size

using Dimension = typename KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>::Dimension

Public Functions

inline const std::vector<index_container_t> &getAllIndices() const: Get a const ref to the internal list of indices; the number of indices is adapted dynamically as the dataset grows in size.

inline explicit KDTreeSingleIndexDynamicAdaptor(const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams &params = KDTreeSingleIndexAdaptorParams(), const size_t maximumPointCount = 1000000000U)

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

explicit KDTreeSingleIndexDynamicAdaptor(const KDTreeSingleIndexDynamicAdaptor<Distance, DatasetAdaptor, DIM, IndexType>&) = delete: Deleted copy constructor

inline void addPoints(IndexType start, IndexType end): Add points to the set, Inserts all points from [start, end]

inline void removePoint(size_t idx): Remove a point from the set (Lazy Deletion)

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

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