kNN-RKD-Tree

Description

This project implements a Randomisd-KD-Tree data structure and algorithm to efficiently guess the k-Nearest-Neighbours on 3D point clouds. It was part of my B.Sc. thesis in Informatics, and extends the work originally started by Dr. Peer Stelldinger. It is a Java-based application, and is recommended to be used in combination with Eclipse. Running the application will open an editor for loading and displaying 3D point clouds (.off format). In the 3D viewer, you can perform different actions like changing lighting conditions, calculating normals, guessing nearest neighbours etc. If you are interested, go play around with it. In the experiments with up to 1mio points, a significant speed up by three orders of magnitude compared to linear search could be observed.