Computer Science > Machine Learning
arXiv:1809.05910 (cs)
[Submitted on 16 Sep 2018 (v1), last revised 13 Feb 2019 (this version, v2)]
Title:MeshCNN: A Network with an Edge
View a PDF of the paper titled MeshCNN: A Network with an Edge, by Rana Hanocka and 4 other authors
View PDFAbstract:Polygonal meshes provide an efficient representation for 3D shapes. They explicitly capture both 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 our task-driven pooling on various learning tasks applied to 3D meshes.
| Comments: | For a two-minute explanation video seethis https URL |
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (stat.ML) |
| Cite as: | arXiv:1809.05910 [cs.LG] |
| (orarXiv:1809.05910v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.1809.05910 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1145/3306346.3322959 DOI(s) linking to related resources |
Submission history
From: Rana Hanocka [view email][v1] Sun, 16 Sep 2018 16:32:29 UTC (5,689 KB)
[v2] Wed, 13 Feb 2019 11:30:57 UTC (26,628 KB)
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