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Recognizing 3D objects in the presence of noise, varying mesh resolution,occlusion and clutter is a very challenging task. This paper presents a novelmethod named Rotational Projection Statistics (RoPS). It has three majormodules: Local Reference Frame (LRF) definition, RoPS feature description and3D object recognition. We propose a novel technique to define the LRF bycalculating the scatter matrix of all points lying on the local surface. RoPSfeature descriptors are obtained by rotationally projecting the neighboringpoints of a feature point onto 2D planes and calculating a set of statistics(including low-order central moments and entropy) of the distribution of theseprojected points. Using the proposed LRF and RoPS descriptor, we present ahierarchical 3D object recognition algorithm. The performance of the proposedLRF, RoPS descriptor and object recognition algorithm was rigorously tested ona number of popular and publicly available datasets. Our proposed techniquesexhibited superior performance compared to existing techniques. We also showedthat our method is robust with respect to noise and varying mesh resolution.Our RoPS based algorithm achieved recognition rates of 100%, 98.9%, 95.4% and96.0% respectively when tested on the Bologna, UWA, Queen's and Ca' FoscariVenezia Datasets.

Rotational Projection Statistics for 3D Local Surface Description and Object Recognition