k-D tree

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.