rvalavi / kd-tree

An implementation of k-d tree for searching

kd-tree

A c++ implementation of k-d tree

Description

• A C++ implementation of k-d tree
• Header-only library
• Provides following Nearest neighbor search functions
  • Nearest neighbor search
  • K-nearest neighbor search
  • Radius search
• Supports both L1 and L2 distances (new feature)

How to use KDTree class

• KDTree class takes a user-defined Point type as its template parameter
• A user-defined Point type needs to satisfy following specifications
  • Implementation of operator[] <= accessor to its coordinates
  • Static member variable DIM <= dimension of the Point
• Passing point cloud to KDTree constructor starts to build k-d tree
• After building k-d tree, you can use Nearest neighbor search functions

#include "kdtree.h"

// user-defined Point type (inherits std::array in order to use operator[])
class MyPoint : public std::array<double, 2>
{
public:

	// dimension of the Point
	static const int DIM = 2;

	// As you want
	// ...
};

int main()
{
	// generate point cloud
	std::vector<MyPoint> points = ...;

	// build k-d tree
	kdt::KDTree<MyPoint> kdtree(points);

	// K-nearest neighbor search (gets indices to neighbors)
	int k = 10;
	MyPoint query = ...;
	std::vector<int> indices = kdtree.knnSearch(query, k);

	return 0;
}

Requirement

• OpenCV (only for running sample code)

Sample code

How to build

$ git clone https://github.com/gishi523/kd-tree.git
$ cd kd-tree
$ mkdir build
$ cd build
$ cmake ../
$ make

How to run

./kdtree [seed]

• seed
  • Seed of rand() in generating random point cloud

Author

gishi523