hmap::KDTreeContext Struct Reference

Context wrapper for KD-tree operations. More...

#include <kd_tree.hpp>

Collaboration diagram for hmap::KDTreeContext:

Public Member Functions
	KDTreeContext (const std::vector< float > &x_, const std::vector< float > &y_)
	Construct the KD-tree context and build the index.
glm::vec2	compte_neighbor_distance_range (size_t k_neighbors) const
	Estimate the range of neighbor distances.
void	neighbor_search (float x_query, float y_query, size_t k_neighbors, std::vector< size_t > &indices, std::vector< float > &distances) const
	Perform a k-nearest neighbor search.
std::vector< nanoflann::ResultItem< unsigned int, float > >	radius_search (float x_query, float y_query, float radius) const
	Perform a radius-based neighbor search.

Public Attributes
const std::vector< float > &	x
const std::vector< float > &	y
NanoflannPointCloudAdaptor	adaptor
KDTree	index

Detailed Description

Context wrapper for KD-tree operations.

This class encapsulates:

• input point data
• nanoflann adaptor
• KD-tree index

It provides convenient methods for nearest-neighbor and radius-based queries.

Note

The input vectors must remain valid for the lifetime of this object.

Constructor & Destructor Documentation

KDTreeContext()

hmap::KDTreeContext::KDTreeContext	(	const std::vector< float > &	x_,
		const std::vector< float > &	y_
	)

Construct the KD-tree context and build the index.

Parameters

x_	Vector of x coordinates
y_	Vector of y coordinates

Member Function Documentation

compte_neighbor_distance_range()

glm::vec2 hmap::KDTreeContext::compte_neighbor_distance_range

(

size_t

k_neighbors

)

const

Estimate the range of neighbor distances.

Computes the minimum and maximum distance to the k-th nearest neighbor across all points in the dataset.

Parameters

k_neighbors

Number of neighbors considered

Returns

vec2(min_distance, max_distance)

neighbor_search()

void hmap::KDTreeContext::neighbor_search	(	float	x_query,
		float	y_query,
		size_t	k_neighbors,
		std::vector< size_t > &	indices,
		std::vector< float > &	distances
	)		const

Perform a k-nearest neighbor search.

Parameters

	x_query	Query x coordinate
	y_query	Query y coordinate
	k_neighbors	Number of neighbors to retrieve
[out]	indices	Output indices of neighbors
[out]	distances	Output squared distances to neighbors

radius_search()

std::vector< nanoflann::ResultItem< unsigned int, float > > hmap::KDTreeContext::radius_search	(	float	x_query,
		float	y_query,
		float	radius
	)		const

Perform a radius-based neighbor search.

Parameters

x_query	Query x coordinate
y_query	Query y coordinate
radius	Search radius (Euclidean distance)

Returns

Vector of (index, squared distance) pairs

Note

Returned distances are squared distances.

Member Data Documentation

x

const std::vector<float>& hmap::KDTreeContext::x

Reference to x coordinates

y

const std::vector<float>& hmap::KDTreeContext::y

Reference to y coordinates

adaptor

NanoflannPointCloudAdaptor hmap::KDTreeContext::adaptor

Nanoflann adaptor

index

KDTree hmap::KDTreeContext::index

KD-tree index

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The documentation for this struct was generated from the following files:

• HighMap/include/highmap/geometry/kd_tree.hpp
• HighMap/src/geometry/kd_tree.cpp