5#include <nanoflann.hpp>
14 const std::vector<Point<T, N>> &
pts;
31template <
typename T,
size_t N>
32using KDTree = nanoflann::KDTreeSingleIndexAdaptor<
33 nanoflann::L2_Simple_Adaptor<T, PointCloudAdaptor<T, N>>,
Definition dbscan_clustering.hpp:11
std::vector< Point< T, N > > random(size_t count, const std::array< std::pair< T, T >, N > &axis_ranges, std::optional< unsigned int > seed=std::nullopt)
Generates a specified number of uniformly distributed random points in N-dimensional space.
Definition random.hpp:66
nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Simple_Adaptor< T, PointCloudAdaptor< T, N > >, PointCloudAdaptor< T, N >, N > KDTree
Definition nanoflann_adaptator.hpp:35
Definition nanoflann_adaptator.hpp:13
size_t kdtree_get_point_count() const
Definition nanoflann_adaptator.hpp:18
bool kdtree_get_bbox(BBOX &) const
Definition nanoflann_adaptator.hpp:25
T kdtree_get_pt(const size_t idx, const size_t dim) const
Definition nanoflann_adaptator.hpp:20
PointCloudAdaptor(const std::vector< Point< T, N > > &points)
Definition nanoflann_adaptator.hpp:16
const std::vector< Point< T, N > > & pts
Definition nanoflann_adaptator.hpp:14
A fixed-size N-dimensional point/vector class.
Definition point.hpp:39