Compact Part-based Shape Spaces for Dense Correspondences

Oliver Burghard, Alexander Berner, Wand Michael, Niloy Mitra, H.-P. Seidel, and Reinhard Klein
Bonn University, 2013
 

Abstract

We consider the problem of establishing dense correspondences within a set of related shapes of strongly varying geometry. For such input, traditional shape matching approaches often produce unsatisfactory results. We propose an ensemble optimization method that improves given coarse correspondences to obtain dense correspondences. Following ideas from minimum description length approaches, it maximizes the compactness of the induced shape space to obtain high-quality correspondences. We make a number of improvements that are important for computer graphics applications: Our approach handles meshes of general topology and handles partial matching between input of varying topology. To this end we introduce a novel part-based generative statistical shape model. We develop a novel analysis algorithm that learns such models from training shapes of varying topology. We also provide a novel synthesis method that can generate new instances with varying part layouts and subject to generic variational constraints. In practical experiments, we obtain a substantial improvement in correspondence quality over state-ofthe- art methods. As example application, we demonstrate a system that learns shape families as assemblies of deformable parts and permits real-time editing with continuous and discrete variability.

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@TECHREPORT{BBWMSK13,
       author = {Burghard, Oliver and Berner, Alexander and Michael, Wand and Mitra, Niloy and Seidel, H.-P. and
                 Klein, Reinhard},
        title = {Compact Part-based Shape Spaces for Dense Correspondences},
      journal = {CoRR},
       volume = {abs/1311.7535},
         year = {2013},
  institution = {Bonn University},
     abstract = {We consider the problem of establishing dense correspondences
                 within a set of related shapes of strongly varying geometry. For such input,
                 traditional shape matching approaches often produce unsatisfactory results.
                 We propose an ensemble optimization method that improves given coarse correspondences
                 to obtain dense correspondences. Following ideas from minimum
                 description length approaches, it maximizes the compactness of the induced
                 shape space to obtain high-quality correspondences. We make a number of
                 improvements that are important for computer graphics applications: Our approach
                 handles meshes of general topology and handles partial matching between
                 input of varying topology. To this end we introduce a novel part-based
                 generative statistical shape model. We develop a novel analysis algorithm that
                 learns such models from training shapes of varying topology. We also provide a
                 novel synthesis method that can generate new instances with varying part layouts
                 and subject to generic variational constraints. In practical experiments,
                 we obtain a substantial improvement in correspondence quality over state-ofthe-
                 art methods. As example application, we demonstrate a system that learns
                 shape families as assemblies of deformable parts and permits real-time editing
                 with continuous and discrete variability.},
          url = {https://cg.cs.uni-bonn.de/en/people/dipl-inform-oliver-burghard/compact-part-based-shape-spaces-for-dense-correspondences/}
}