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Graph."],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>\n            Convolutionalneural networks (CNNs) have made great breakthroughs in two-dimensional (2D) computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provides a hierarchical multi-resolution structure in which each face in a closed 2-manifold triangle mesh is exactly adjacent to three faces. Motivated by these two observations, this article presents\n            <jats:italic>SubdivNet<\/jats:italic>\n            , an innovative and versatile CNN framework for three-dimensional (3D) triangle meshes with Loop subdivision sequence connectivity. Making an analogy between mesh faces and pixels in a 2D image allows us to present a mesh convolution operator to aggregate local features from nearby faces. By exploiting face neighborhoods, this convolution can support standard 2D convolutional network concepts, e.g., variable kernel size, stride, and dilation. Based on the multi-resolution hierarchy, we make use of pooling layers that uniformly merge four faces into one and an upsampling method that splits one face into four. Thereby, many popular 2D CNN architectures can be easily adapted to process 3D meshes. Meshes with arbitrary connectivity can be remeshed to have Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach. Extensive evaluation and various applications demonstrate SubdivNet\u2019s effectiveness and efficiency.\n          <\/jats:p>","DOI":"10.1145\/3506694","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T07:04:20Z","timestamp":1646723060000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":118,"title":["Subdivision-based Mesh Convolution Networks"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7507-6542","authenticated-orcid":false,"given":"Shi-Min","family":"Hu","sequence":"first","affiliation":[{"name":"BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6643-6016","authenticated-orcid":false,"given":"Zheng-Ning","family":"Liu","sequence":"additional","affiliation":[{"name":"BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng-Hao","family":"Guo","sequence":"additional","affiliation":[{"name":"BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Xiong","family":"Cai","sequence":"additional","affiliation":[{"name":"BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Huang","sequence":"additional","affiliation":[{"name":"BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tai-Jiang","family":"Mu","sequence":"additional","affiliation":[{"name":"BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralph R.","family":"Martin","sequence":"additional","affiliation":[{"name":"Cardiff University, UK and Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"e_1_3_2_2_1","unstructured":"Adobe.com. 2021. 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