Model Selection for Composite Objects with Attribute Grammars
نویسندگان
چکیده
In this paper we present a concept for the reconstruction of composite objects from LIDAR 3D point clouds with focus on symmetric and partly recursive structures. The concept is top-down and bases on a generative model given by an attribute grammar. It uses the well known random sample consensus paradigm for model selection. The attribute grammars formalism is used for the modeling of 3D objects. It models composition by production rules and it can also incorporate constraints on parameters and geometric dependencies between parts by attributes. The constraints which are given by the grammar advise to preprocess the data in order to improve the RANSAC algorithm. In the preprocessing step the normal vector of each plane defined by a point and his n neighbors is estimated. The samples are drawn from a subset of these ‘needles’.
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