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Modeling the Space of Point Landmark Constrained Diffeomorphisms

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Computer Vision – ECCV 2020 (ECCV 2020)

Abstract

Surface registration plays a fundamental role in shape analysis and geometric processing. Generally, there are three criteria in evaluating a surface mapping result: diffeomorphism, small distortion, and feature alignment. To fulfill these requirements, this work proposes a novel model of the space of point landmark constrained diffeomorphisms. Based on Teichmüller theory, this mapping space is generated by the Beltrami coefficients, which are infinitesimally Teichmüller equivalent to 0. These Beltrami coefficients are the solutions to a linear equation group. By using this theoretic model, optimal registrations can be achieved by iterative optimization with linear constraints in the diffeomorphism space, such as harmonic maps and Teichmüller maps, which minimize different types of distortion. The theoretical model is rigorous and has practical value. Our experimental results demonstrate the efficiency and efficacy of the proposed method.

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Acknowledgements

The project is partially supported by NSF CMMI-1762287, NSF DMS-1737812 and Ford URP.

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Wen, C., Guo, Y., Gu, X. (2020). Modeling the Space of Point Landmark Constrained Diffeomorphisms. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12375. Springer, Cham. https://doi.org/10.1007/978-3-030-58577-8_22

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  • DOI: https://doi.org/10.1007/978-3-030-58577-8_22

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