{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T08:05:28Z","timestamp":1774080328962,"version":"3.50.1"},"reference-count":115,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            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 &amp; pooling to suit\n            <jats:italic>Convolutional Neural Networks (CNNs).<\/jats:italic>\n            This paper proposes a very different approach, termed\n            <jats:italic>MeshWalker<\/jats:italic>\n            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\n            <jats:italic>Recurrent Neural Network (RNN)<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/3414685.3417806","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T21:51:05Z","timestamp":1606513865000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":121,"title":["MeshWalker"],"prefix":"10.1145","volume":"39","author":[{"given":"Alon","family":"Lahav","sequence":"first","affiliation":[{"name":"Technion - Israel Institute of Technology"}]},{"given":"Ayellet","family":"Tal","sequence":"additional","affiliation":[{"name":"Technion - Israel Institute of Technology"}]}],"member":"320","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Adobe. 2016. Adobe Fuse 3D Characters. https:\/\/www.mixamo.com.  Adobe. 2016. 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