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Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

We present a learning-based method for interpolating and manipulating 3D shapes represented as point clouds, that is explicitly designed to preserve intrinsic shape properties. Our approach is based on constructing a dual encoding space that enables shape synthesis and, at the same time, provides links to the intrinsic shape information, which is typically not available on point cloud data. Our method works in a single pass and avoids expensive optimization, employed by existing techniques. Furthermore, the strong regularization provided by our dual latent space approach also helps to improve shape recovery in challenging settings from noisy point clouds across different datasets. Extensive experiments show that our method results in more realistic and smoother interpolations compared to baselines. Both the code and our pre-trained network can be found online: https://github.com/mrakotosaon/intrinsic_interpolations.

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Acknowledgements

Parts of this work were supported by the KAUST CRG-2017-3426 Award and the ERC Starting Grant No. 758800 (EXPROTEA).

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Authors and Affiliations

  1. LIX, Ecole Polytechnique, IP Paris, Palaiseau, France

    Marie-Julie Rakotosaona & Maks Ovsjanikov

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  1. Marie-Julie Rakotosaona
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  2. Maks Ovsjanikov
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Correspondence to Marie-Julie Rakotosaona .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Rakotosaona, MJ., Ovsjanikov, M. (2020). Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12347. Springer, Cham. https://doi.org/10.1007/978-3-030-58536-5_39

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  • DOI: https://doi.org/10.1007/978-3-030-58536-5_39

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