#include <knn_cloud.h>

Public Member Functions
	knn_cloud (const double epsilon=0.01)
	~knn_cloud ()
void	refresh (const std::vector< vector3 > &points)
double	nearest_distance (const vector3 &p) const
size_t	knn_search (size_t k, const vector3 &p, std::vector< size_t > &indices, std::vector< double > &distances) const
bool	is_valid () const
vector3	operator[] (const size_t idx) const
size_t	size () const

Detailed Description

A basic wrapper for 3D points around the ANN library for nearest neighbour search.

Constructor & Destructor Documentation

knn_cloud::knn_cloud ( const double epsilon = 0.01 ) [inline]

Parameters:

[in] epsilon The accepted error in the nearest neighbor search. See the ANN documentation more details.

knn_cloud::~knn_cloud ( ) [inline]

bool knn_cloud::is_valid ( ) const [inline]

Find K nearest neighbors in the database.

Parameters:

`[in]`	k	The number of neighbors to be found.
`[in]`	p	The query point.
`[out]`	indices	The indices of the found neighbors. These are correspond to the data that was given when the database was created.
`[out]`	distances	The distances to the neighbors.

Returns:: The number of found points. This can be lower than k if the databse is small.

double knn_cloud::nearest_distance ( const vector3 & p ) const [inline]

Find the distance to the closest point in the database.

Parameters:

[in] p The query point.

Returns:: The distance to the point in the database that is closest to the input.

vector3 knn_cloud::operator[] ( const size_t idx ) const [inline]

Index into the database.

Parameters:

[in] idx The point index. This corresponds to the indices used when the database was created.

void knn_cloud::refresh ( const std::vector< vector3 > & points ) [inline]

Populate the data structure with data points.

Parameters:

[in] points A list of 3D points to be inserted in the KD-tree.

size_t knn_cloud::size ( ) const [inline]

The documentation for this class was generated from the following file: