Similarity-based viewspace interpolation and the categorization of 3D objects

Shimon Edelman
School of Cognitive and Computing Sciences
University of Sussex, Falmer, Brighton BN1 9QH, UK

Sharon Duvdevani-Bar
Department of Applied Mathematics
Weizmann Institute of Science, Rehovot 76100, Israel

Abstract Visual objects can be represented by their similarities to a small number of reference shapes or prototypes. This method yields low-dimensional (and therefore computationally tractable) representations, which support both the recognition of familiar shapes and the categorization of novel ones. In this note, we show how such representations can be used in a variety of tasks involving novel objects: viewpoint-invariant recognition, recovery of a canonical view, estimation of pose, and prediction of an arbitrary view. The unifying principle in all these cases is the representation of the view space of the novel object as an interpolation of the view spaces of the reference shapes.