Abstract

This paper presents a new method for detecting
and merging redundant points in registered range data.
Given a global representation from sequences of 3D
points, the points are projected onto a virtual image
plane computed from the intrinsic parameters of the
sensor. Candidates for redundancy are collected per
pixel which then are clustered locally via region growing and replaced by the cluster’s mean value. As data is
provided in a certain manner defined by camera characteristics, this processing step preserves the structural
information of the data. For evaluation, our approach is
compared to two other algorithms. Applied to two dif-
ferent sequences, it is shown that the presented method
gives smooth results within planar regions of the point
clouds by successfully reducing noise and redundancy
and thus improves registered range data.