The pcl_kdtree library provides the kd-tree data-structure, using FLANN, that allows for fast nearest neighbor searches.
A Kd-tree (k-dimensional tree) is a space-partitioning data structure that stores a set of k-dimensional points in a tree structure that enables efficient range searches and nearest neighbor searches. Nearest neighbor searches are a core operation when working with point cloud data and can be used to find correspondences between groups of points or feature descriptors or to define the local neighborhood around a point or points.
Classes | |
class | pcl::KdTree< PointT > |
KdTree represents the base spatial locator class for nearest neighbor estimation. More... | |
class | pcl::KdTreeFLANN< PointT > |
KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. More... | |
class | pcl::OrganizedDataIndex< PointT > |
OrganizedDataIndex is a type of spatial locator used to query organized datasets, such as point clouds acquired using dense stereo devices. More... | |
Functions | |
template<typename PointT > | |
void | pcl::initTree (const int &spatial_locator, boost::shared_ptr< pcl::KdTree< PointT > > &tree, int k=0) |
Initialize the spatial locator used for nearest neighbor search. |
void pcl::initTree | ( | const int & | spatial_locator, | |
boost::shared_ptr< pcl::KdTree< PointT > > & | tree, | |||
int | k = 0 | |||
) |
Initialize the spatial locator used for nearest neighbor search.
spatial_locator | the type of spatial locator to construct (0 = FLANN, 1 = organized) | |
tree | the resultant tree as a boost shared pointer | |
k | optional parameter (default 0) applied only if the spatial locator is set to organized (1) |
Definition at line 47 of file tree_types.hpp.