Non-parametric segmentation of ALS point clouds using mean shift

TLDR: In this paper, a self-contained derivation of the mean shift procedure is given, and how it can be used to obtain a classification or segmentation of an unstructured 3D point cloud.

Abstract: Abstract Segmentation is a key task in the processing of 3D point clouds as obtained from airborne laser scanners (ALS). However, most of the segmentation techniques currently employed require prior gridding of the data and thus do not respect the inherently three-dimensional geometry of more intricate structures such as power lines. By contrast, the mean shift procedure, a filtering and clustering approach which has recently found much interest in the image processing community, works directly on the original 3D point cloud; also, mean shift is a non-parametric technique (i.e., it does not depend on any geometric model assumptions) and can thus also be applied to vegetation structures. In this paper, we will give a self-contained derivation of the mean shift procedure, and discuss how it can be used to obtain a classification or segmentation of an unstructured 3D point cloud. Two application examples shall further illustrate its usefulness to ALS data processing.