Reducing noise and redundancy in registered range data for planar surface extraction

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 clusterpsilas 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 different 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.