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/PointCloudLibraryDetailed Description
Overview
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.
Requirements
Classes | |
| class | pcl::KdTree< PointT > |
|
KdTree represents the base spatial locator class for kd-tree implementations. More... |
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| class | pcl::KdTreeFLANN< PointT, Dist > |
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KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. More... |
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Functions | |
| template<typename PointT > | |
| void | pcl::getApproximateIndices (const typename pcl::PointCloud< PointT >::ConstPtr &cloud_in, const typename pcl::PointCloud< PointT >::ConstPtr &cloud_ref, Indices &indices) |
| Get a set of approximate indices for a given point cloud into a reference point cloud. More... |
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| template<typename Point1T , typename Point2T > | |
| void | pcl::getApproximateIndices (const typename pcl::PointCloud< Point1T >::ConstPtr &cloud_in, const typename pcl::PointCloud< Point2T >::ConstPtr &cloud_ref, Indices &indices) |
| Get a set of approximate indices for a given point cloud into a reference point cloud. More... |
|
Function Documentation
getApproximateIndices() [1/2]
| void pcl::getApproximateIndices | ( | const typename pcl::PointCloud< Point1T >::ConstPtr & | cloud_in, |
| const typename pcl::PointCloud< Point2T >::ConstPtr & | cloud_ref, | ||
| Indices & | indices | ||
| ) |
#include <pcl/kdtree/io.h>
Get a set of approximate indices for a given point cloud into a reference point cloud.
The coordinates of the two point clouds can differ. The method uses an internal KdTree for finding the closest neighbors from cloud_in in cloud_ref.
- Parameters
-
[in] cloud_in the input point cloud dataset [in] cloud_ref the reference point cloud dataset [out] indices the resultant set of nearest neighbor indices of cloud_in in cloud_ref
Definition at line 47 of file io.hpp.
References pcl::KdTree< PointT >::nearestKSearchT(), pcl::KdTreeFLANN< PointT, Dist >::setInputCloud(), and pcl::PointCloud< PointT >::size().
getApproximateIndices() [2/2]
| void pcl::getApproximateIndices | ( | const typename pcl::PointCloud< PointT >::ConstPtr & | cloud_in, |
| const typename pcl::PointCloud< PointT >::ConstPtr & | cloud_ref, | ||
| Indices & | indices | ||
| ) |
#include <pcl/kdtree/io.h>
Get a set of approximate indices for a given point cloud into a reference point cloud.
The coordinates of the two point clouds can differ. The method uses an internal KdTree for finding the closest neighbors from cloud_in in cloud_ref.
- Parameters
-
[in] cloud_in the input point cloud dataset [in] cloud_ref the reference point cloud dataset [out] indices the resultant set of nearest neighbor indices of cloud_in in cloud_ref
Definition at line 67 of file io.hpp.
References pcl::KdTreeFLANN< PointT, Dist >::nearestKSearch(), pcl::KdTreeFLANN< PointT, Dist >::setInputCloud(), and pcl::PointCloud< PointT >::size().
© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/group__kdtree.html