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In this paper we introduce an algorithm for 3D motion estimation in point clouds that is based on Chasles' kinematic theorem. The proposed algorithm estimates 3D motion parameters directly from the data by exploiting the geometry of rigid transformation using an evidence gathering technique in a Hough-voting-like approach. The algorithm provides an alternative to the feature description and matching pipelines commonly used by numerous 3D object recognition and registration algorithms, as itdoi:10.1109/icpr.2016.7899890 dblp:conf/icpr/AbuzainaNC16 fatcat:ang25u2pl5hs3da2cgc7otqiu4