Irregular body surface description using planes defined from noisy 3D point clouds.

This paper presents an horizontal technique to classify the main directions of orientation of the planar surfaces that conform solid bodies. When mathematically representing these surfaces via 3D point clouds, a cascade or chain process using combined information of the point positions and of their detected normal vectors is applied to solidly identify the planar surfaces composing them and to retrieve their orientations. This technique distinguishes itself from others in the field due to (i) a specific filtering inspired by image processing techniques that aims to mitigate the effects that surface rugosity-induced mathematical noise can induce on the classification; and (ii) also due to the data representation on the unit-sphere using the von Mises–Fisher mixture model. The extents and limits of the proposed process are presented using several validation cases of increasing complexity, and its results and possibilities for horizontal applications over many kinds of real-life bodies are also analysed. • A system joining cross-field techniques classifies orientations in 3D point clouds. • Rugosity in real surfaces equals image noise affecting the orientations. • Denoised sphere orientations is a robust framework for orientation segmentation. • This technology combination provides an expert solution applicable to different fields. [ABSTRACT FROM AUTHOR]