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Cited by 7423D Shape Modeling and AnalysisComputer Graphics and Visualization TechniquesAdvanced Neural Network Applications

MeshCNN: a network with an edge

Open access
Hanocka, Rana, Hertz, Amir, Fish, Noa, Giryes, Raja, Fleishman, Shachar, Cohen-Or, Daniel·2019·ACM Transactions on Graphics
DOI 10.1145/3306346.3322959
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Abstract

Polygonal meshes provide an efficient representation for 3D shapes. They explicitly captureboth shape surface and topology, and leverage non-uniformity to represent large flat regions as well as sharp, intricate features. This non-uniformity and irregularity, however, inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations. In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN , a convolutional neural network designed specifically for triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized convolution and pooling layers that operate on the mesh edges, by leveraging their intrinsic geodesic connections. Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions. MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones. We demonstrate the effectiveness of MeshCNN on various learning tasks applied to 3D meshes.

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  • Our system can handle inputs which contain boundaries, self-intersections, or disconnected components.

    “As outlined in MeshCNN [Hanocka et al. 2019], our system can handle inputs which contain boundaries, self-intersections, or disconnected components.”

    From Learning skeletal articulations with neural blend shapes (2021) · cites this paper as Hanocka et al. 2019

    The Overview (p. 4) says MeshCNN assumes manifold meshes that may have boundary edges, supporting the boundary portion of the claim. The passages found for self-intersections and disconnected components do not say the system handles either; the discussion of intersecting 1-rings in the edge-collapse passage (p. 6) is not a claim about self-intersecting input meshes. The source also describes MeshCNN, not the citing authors’ system. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cites

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Cited by

Sorted by citations

Authors
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., & Cohen-Or, D.
Affiliations
  • Tel Aviv University (IL)
  • Amazon (United States) (US)
  • Tel Aviv University
ORCID
Venue
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Published
2019-07-12
Volume
38
Issue
4
Pages
1-12
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
DOI
10.1145/3306346.3322959
OpenAlex
W2891396148
Cited by
742
References
7
Citations by year
2026: 93 · 2025: 119 · 2024: 129 · 2023: 123 · 2022: 95 · 2021: 108 · 2020: 67 · 2019: 8
FWCI
47.60
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Transactions on Graphics (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
ERC-StG (757497)
Topics
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
polygonal meshes, triangular meshes, 3D shape analysis, edge convolution, edge collapse, mesh connectivity

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