{"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/meshnet-mesh-neural-network-for-3d-shape","title":"MeshNet: Mesh Neural Network for 3D Shape Representation","arxiv_id":"1811.11424","date":"2018-11-28","proceeding":null,"authors":["Yutong Feng","Yifan Feng","Haoxuan You","Xibin Zhao","Yue Gao"],"abstract":"Mesh is an important and powerful type of data for 3D shapes and widely\nstudied in the field of computer vision and computer graphics. Regarding the\ntask of 3D shape representation, there have been extensive research efforts\nconcentrating on how to represent 3D shapes well using volumetric grid,\nmulti-view and point cloud. However, there is little effort on using mesh data\nin recent years, due to the complexity and irregularity of mesh data. In this\npaper, we propose a mesh neural network, named MeshNet, to learn 3D shape\nrepresentation from mesh data. In this method, face-unit and feature splitting\nare introduced, and a general architecture with available and effective blocks\nare proposed. In this way, MeshNet is able to solve the complexity and\nirregularity problem of mesh and conduct 3D shape representation well. We have\napplied the proposed MeshNet method in the applications of 3D shape\nclassification and retrieval. Experimental results and comparisons with the\nstate-of-the-art methods demonstrate that the proposed MeshNet can achieve\nsatisfying 3D shape classification and retrieval performance, which indicates\nthe effectiveness of the proposed method on 3D shape representation.","url_abs":"http://arxiv.org/abs/1811.11424v1","url_pdf":"http://arxiv.org/pdf/1811.11424v1.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":"meshnet-mesh-neural-network-for-3d-shape","repo_url":"https://github.com/sudhir5595/Mesh_Neural_Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"meshnet-mesh-neural-network-for-3d-shape","repo_url":"https://github.com/iMoonLab/MeshNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-shape-retrieval","task_name":"3D Shape Classification"},{"task_slug":"3d-shape-representation","task_name":"3D Shape Representation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.11424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11424"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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