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Implicit Field Supervision for Robust Non-rigid Shape Matching

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13663))

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Abstract

Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing. Existing methods often show weak resilience when presented with challenges innate to real-world data such as noise, outliers, self-occlusion etc. On the other hand, auto-decoders have demonstrated strong expressive power in learning geometrically meaningful latent embeddings. However, their use in shape analysis has been limited. In this paper, we introduce an approach based on an auto-decoder framework, that learns a continuous shape-wise deformation field over a fixed template. By supervising the deformation field for points on-surface and regularizing for points off-surface through a novel Signed Distance Regularization (SDR), we learn an alignment between the template and shape volumes. Trained on clean water-tight meshes, without any data-augmentation, we demonstrate compelling performance on compromised data and real-world scans (Our code is available at https://github.com/Sentient07/IFMatch).

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Authors and Affiliations

  1. LIX, École Polytechnique, Paris, IP, France

    Ramana Sundararaman, Gautam Pai & Maks Ovsjanikov

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  1. Ramana Sundararaman
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  2. Gautam Pai
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  3. Maks Ovsjanikov
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Correspondence to Ramana Sundararaman .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Sundararaman, R., Pai, G., Ovsjanikov, M. (2022). Implicit Field Supervision for Robust Non-rigid Shape Matching. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13663. Springer, Cham. https://doi.org/10.1007/978-3-031-20062-5_20

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