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.

Automated summary

This paper presents a horizontal technique for classifying the main directions of orientation of planar surfaces that make up solid bodies. By mathematically representing these surfaces using 3D point clouds and detecting their normal vectors, the technique accurately identifies the planar surfaces and retrieves their orientations. It distinguishes itself from other techniques in the field by applying specific filtering inspired by image processing techniques to mitigate the effects of mathematical noise caused by surface roughness, and by representing the data on the unit-sphere using the von Mises-Fisher mixture model. The proposed technique is evaluated using various validation cases of increasing complexity and its potential applications for a wide range of real-life bodies are analyzed.