Clustering appearances of 3D objects

We introduce a method for unsupervised clustering of images of 3D objects. Our method examines the space of all images and partitions the images into sets that form smooth and parallel surfaces in this space. It further uses sequences of images to obtain more reliable clustering. Finally, since our method relies on a non-Euclidean similarity measure we introduce algebraic techniques for estimating local properties of these surfaces without first embedding the images in a Euclidean space. We demonstrate our method by applying it to a large database of images