Feature relevance assessment for the semantic interpretation of 3D point cloud data
- Publisher: KIT, Karlsruhe
(issn: 1682-1750 1682-1750)
The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision.
In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance
assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D
points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure
which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally
applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses
different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the
great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results
reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene
interpretation can be carried out with only few versatile features and even improved accuracy.