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

Feature relevance assessment for the semantic interpretation of 3D point cloud data