Abstract
The registration of non-rigidly deforming shapes is a fundamental problem in the area of Graphics and Computational Geometry. One of the applications of shape registration is to facilitate 3D model retrieval; after alignment it becomes easier to compare shapes since the correspondences between their elements is known. Many existing methods have been proposed for computing shape correspondence. These approaches assume surface deformations to be either: piecewise rigid, (near-)isometric or topologically consistent. Presently, there are only a few public benchmarking shape correspondence datasets that challenge these assumptions about deformations. Previous contests have used synthetic objects that produce deformations that are not realistic. Bogo et al. [2014], Andrews et al. [2018] do capture real-life objects, they focus on specific object categories (human bodies and human faces), and neither benchmark suitably considers the different types of deformation that an object may undergo. This motivates our competition which will provide of a new benchmark that consists of real-life shapes that undergo different types of deformations in a controlled manner.
