Efficiently consistent affinity propagation for 3D shapes co-segmentation

Abstract

Unsupervised co-segmentation for a set of 3D shapes is a challenging problem as no prior information is provided. The accuracy of the current approaches is necessarily restricted by the accuracy of the unsupervised face classification, which is used to provide an initialization for the following optimization to improve the consistency between adjacent faces. However, it is exceedingly difficult to obtain a satisfactory initialization pre-segmentation owing to variation in topology and geometry of 3D shapes. In this study, we consider the unsupervised 3D shape co-segmentation as an exemplar-based clustering problem, aimed at simultaneously discovering optimal exemplars and obtaining co-segmentation results. Therefore, we introduce a novel exemplar-based clustering method based on affinity propagation for 3D shape co-segmentation, which can automatically identify representative exemplars and patterns in 3D shapes considering the high-order statistics, yielding consistent and accurate co-segmentation results. Experiments using various datasets, especially large sets with 200 or more shapes that would be challenging to manually segment, demonstrate that our method exhibits a better performance compared to state-of-the-art methods.