k-D tree

Perform closest point search or range query using a k-D tree implementation.
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Updated 29 Oct 2013

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This distribution contains the KDTREE, KDTREEIDX, and KDRANGEQUERY functions.

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KDTREE Find closest points using a k-D tree.

CP = KDTREE( REFERENCE, MODEL ) finds the closest points in
REFERENCE for each point in MODEL. The search is performed in an efficient manner by building a k-D tree from the datapoints in REFERENCE, and querying the tree for each datapoint in MODEL.

IDX = KDTREEIDX( REFERENCE, MODEL ) finds the closest points in REFERENCE for each point in MODEL. The search is performed in an efficient manner by building a k-D tree from the datapoints in REFERENCE, and querying the tree for each datapoint in MODEL.

PTS = KDRANGEQUERY( ROOT, QUERYPT, DISTLIM ) finds all the points stored in the k-D tree ROOT that are within DISTLIM units from the QUERYPT. Proximity is quantified using a D-dimensional Euclidean (2-norm) distance.
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Two demo scripts are provided (kdtree_demo.m & kdrange_demo.m).

You will need to compile the code in the kdtree/src library using the
MATLAB mex compiler. Place the compiled mex files in the kdtree/lib directory. Finally, add the kdtree/lib directory to your MATLAB path.

** Refer to the README file for more detailed instructions.

Cite As

Guy Shechter (2024). k-D tree (https://www.mathworks.com/matlabcentral/fileexchange/4586-k-d-tree), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2013b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
1.2.0.0

More detailed instructions on how to create the mex runtimes.

1.1.0.0

More detailed instructions on how to create the mex runtimes.