{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/meshcnn-a-network-with-an-edge","title":"MeshCNN: A Network with an Edge","arxiv_id":"1809.05910","date":"2018-09-16","proceeding":null,"authors":["Rana Hanocka","Amir Hertz","Noa Fish","Raja Giryes","Shachar Fleishman","Daniel Cohen-Or"],"abstract":"Polygonal meshes provide an efficient representation for 3D shapes. They\nexplicitly capture both shape surface and topology, and leverage non-uniformity\nto represent large flat regions as well as sharp, intricate features. This\nnon-uniformity and irregularity, however, inhibits mesh analysis efforts using\nneural networks that combine convolution and pooling operations. In this paper,\nwe utilize the unique properties of the mesh for a direct analysis of 3D shapes\nusing MeshCNN, a convolutional neural network designed specifically for\ntriangular meshes. Analogous to classic CNNs, MeshCNN combines specialized\nconvolution and pooling layers that operate on the mesh edges, by leveraging\ntheir intrinsic geodesic connections. Convolutions are applied on edges and the\nfour edges of their incident triangles, and pooling is applied via an edge\ncollapse operation that retains surface topology, thereby, generating new mesh\nconnectivity for the subsequent convolutions. MeshCNN learns which edges to\ncollapse, thus forming a task-driven process where the network exposes and\nexpands the important features while discarding the redundant ones. We\ndemonstrate the effectiveness of our task-driven pooling on various learning\ntasks applied to 3D meshes.","url_abs":"http://arxiv.org/abs/1809.05910v2","url_pdf":"http://arxiv.org/pdf/1809.05910v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"meshcnn-a-network-with-an-edge","repo_url":"https://github.com/ranahanocka/MeshCNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-object-classification","task_name":"3D Object Classification"},{"task_slug":"3d-part-segmentation","task_name":"3D Part Segmentation"},{"task_slug":"cube-engraving-classification","task_name":"Cube Engraving Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1809.05910","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}