PointTriNet: Learned Triangulation of 3D Point Sets

Nicholas Sharp, Maks Ovsjanikov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2020
PublisherSpringer International Publishing
Number of pages17
ISBN (Print)9783030585914
StatePublished - Nov 3 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-30
Acknowledged KAUST grant number(s): CRG-2017-3426
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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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