{"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/texturenet-consistent-local-parametrizations","title":"TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes","arxiv_id":"1812.00020","date":"2018-11-30","proceeding":"CVPR 2019 6","authors":["Jingwei Huang","Haotian Zhang","Li Yi","Thomas Funkhouser","Matthias Nießner","Leonidas Guibas"],"abstract":"We introduce, TextureNet, a neural network architecture designed to extract\nfeatures from high-resolution signals associated with 3D surface meshes (e.g.,\ncolor texture maps). The key idea is to utilize a 4-rotational symmetric\n(4-RoSy) field to define a domain for convolution on a surface. Though 4-RoSy\nfields have several properties favorable for convolution on surfaces (low\ndistortion, few singularities, consistent parameterization, etc.), orientations\nare ambiguous up to 4-fold rotation at any sample point. So, we introduce a new\nconvolutional operator invariant to the 4-RoSy ambiguity and use it in a\nnetwork to extract features from high-resolution signals on geodesic\nneighborhoods of a surface. In comparison to alternatives, such as PointNet\nbased methods which lack a notion of orientation, the coherent structure given\nby these neighborhoods results in significantly stronger features. As an\nexample application, we demonstrate the benefits of our architecture for 3D\nsemantic segmentation of textured 3D meshes. The results show that our method\noutperforms all existing methods on the basis of mean IoU by a significant\nmargin in both geometry-only (6.4%) and RGB+Geometry (6.9-8.2%) settings.","url_abs":"http://arxiv.org/abs/1812.00020v2","url_pdf":"http://arxiv.org/pdf/1812.00020v2.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":"texturenet-consistent-local-parametrizations","repo_url":"https://github.com/hjwdzh/TextureNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-semantic-segmentation","task_name":"3D Semantic Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-scannet","task":"Semantic Segmentation","dataset":"ScanNet","model":"TextureNet","rank_in_archive_order":37,"of":45,"metrics":{"test mIoU":"56.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.00020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00020"}},"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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