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PointTriNet: Learned Triangulation of 3D Point Sets

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12368))

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Abstract

This work considers a new task in geometric deep learning: generating a triangulation among a set of points in 3D space. We present PointTriNet, a differentiable and scalable approach enabling point set triangulation as a layer in 3D learning pipelines. The method iteratively applies two neural networks: a classification network predicts whether a candidate triangle should appear in the triangulation, while a proposal network suggests additional candidates. Both networks are structured as PointNets over nearby points and triangles, using a novel triangle-relative input encoding. Since these learning problems operate on local geometric data, our method is efficient and scalable, and generalizes to unseen shape categories. Our networks are trained in an unsupervised manner from a collection of shapes represented as point clouds. We demonstrate the effectiveness of this approach for classical meshing tasks, robustness to outliers, and as a component in end-to-end learning systems.

Code is available at github.com/nmwsharp/learned-triangulation.

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Acknowledgements

The authors are grateful to Marie-Julie Rakotosaona and Keenan Crane for fruitful initial discussions, and to Angela Dai for assistance comparing with Scan2Mesh. Parts of this work were supported by an NSF Graduate Research Fellowship, the KAUST OSR Award No. CRG-2017-3426 and the ERC Starting Grant No. 758800 (EXPROTEA).

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, USA

    Nicholas Sharp

  2. LIX, École Polytechnique, IP Paris, Palaiseau, France

    Maks Ovsjanikov

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  1. Nicholas Sharp
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Correspondence to Nicholas Sharp .

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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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Sharp, N., Ovsjanikov, M. (2020). PointTriNet: Learned Triangulation of 3D Point Sets. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_45

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  • DOI: https://doi.org/10.1007/978-3-030-58592-1_45

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