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Generalized Connectivity Constraints for Spatio-temporal 3D Reconstruction

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 8692))

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Abstract

This paper introduces connectivity preserving constraints into spatio-temporal multi-view reconstruction. We efficiently model connectivity constraints by precomputing a geodesic shortest path tree on the occupancy likelihood. Connectivity of the final occupancy labeling is ensured with a set of linear constraints on the labeling function. In order to generalize the connectivity constraints from objects with genus 0 to an arbitrary genus, we detect loops by analyzing the visual hull of the scene. A modification of the constraints ensures connectivity in the presence of loops. The proposed efficient implementation adds little runtime and memory overhead to the reconstruction method. Several experiments show significant improvement over state-of-the-art methods and validate the practical use of this approach in scenes with fine structured details.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Technische Universität München, Boltzmannstr. 3, 85748, Garching, Germany

    Martin Ralf Oswald, Jan Stühmer & Daniel Cremers

Authors
  1. Martin Ralf Oswald

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  2. Jan Stühmer

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  3. Daniel Cremers

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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Toronto, 6 King’s College Road, M5H 3S5, Toronto, ON, Canada

    David Fleet

  2. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague, Technicka 2, 166 27, Prague 6, Czech Republic

    Tomas Pajdla

  3. Max-Planck-Institut für Informatik, Campus E1 4, 66123, Saarbrücken, Germany

    Bernt Schiele

  4. KU Leuven, ESAT - PSI, iMinds, Kasteelpark Arenberg 10, Bus 2441, 3001, Leuven, Belgium

    Tinne Tuytelaars

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© 2014 Springer International Publishing Switzerland

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Oswald, M.R., Stühmer, J., Cremers, D. (2014). Generalized Connectivity Constraints for Spatio-temporal 3D Reconstruction. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8692. Springer, Cham. https://doi.org/10.1007/978-3-319-10593-2_3

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