{"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/meshwalker-deep-mesh-understanding-by-random","title":"MeshWalker: Deep Mesh Understanding by Random Walks","arxiv_id":"2006.05353","date":"2020-06-09","proceeding":null,"authors":["Alon Lahav","Ayellet Tal"],"abstract":"Most attempts to represent 3D shapes for deep learning have focused on volumetric grids, multi-view images and point clouds. In this paper we look at the most popular representation of 3D shapes in computer graphics - a triangular mesh - and ask how it can be utilized within deep learning. The few attempts to answer this question propose to adapt convolutions & pooling to suit Convolutional Neural Networks (CNNs). This paper proposes a very different approach, termed MeshWalker, to learn the shape directly from a given mesh. The key idea is to represent the mesh by random walks along the surface, which \"explore\" the mesh's geometry and topology. Each walk is organized as a list of vertices, which in some manner imposes regularity on the mesh. The walk is fed into a Recurrent Neural Network (RNN) that \"remembers\" the history of the walk. We show that our approach achieves state-of-the-art results for two fundamental shape analysis tasks: shape classification and semantic segmentation. Furthermore, even a very small number of examples suffices for learning. This is highly important, since large datasets of meshes are difficult to acquire.","url_abs":"https://arxiv.org/abs/2006.05353v3","url_pdf":"https://arxiv.org/pdf/2006.05353v3.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":"meshwalker-deep-mesh-understanding-by-random","repo_url":"https://github.com/AlonLahav/MeshWalker","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-classification","task_name":"3D Classification"},{"task_slug":"3d-object-recognition","task_name":"3D Object Recognition"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-recognition-on-cube-engraving","task":"3D Object Recognition","dataset":"Cube Engraving","model":"MeshWalker (ours)","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"98.6"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-recognition-on-modelnet40","task":"3D Object Recognition","dataset":"ModelNet40","model":"MeshWalker (ours)","rank_in_archive_order":4,"of":6,"metrics":{"Accuracy":"92.3%"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-recognition-on-shrec11-split10-10","task":"3D Object Recognition","dataset":"SHREC11, Split10-10","model":"MeshWalker (ours)","rank_in_archive_order":1,"of":1,"metrics":{"Per-Class Accuracy":"97.1"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-recognition-on-shrec11-split16-4","task":"3D Object Recognition","dataset":"SHREC11, Split16-4","model":"MeshWalker (ours)","rank_in_archive_order":1,"of":1,"metrics":{"Per-Class Accuracy":"98.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}