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PointTriNet: Learned Triangulation of 3D Point Sets

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.
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Contributor : Christophe Lino Connect in order to contact the contributor
Submitted on : Tuesday, December 8, 2020 - 1:53:23 PM
Last modification on : Wednesday, December 9, 2020 - 3:39:27 AM

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  • HAL Id : hal-03046427, version 1
  • ARXIV : 2005.02138



Nicholas Sharp, Maks Ovsjanikov. PointTriNet: Learned Triangulation of 3D Point Sets. ECCV, Aug 2020, Online, France. ⟨hal-03046427⟩



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