{"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/geometric-features-for-voxel-based-surface","title":"Geometric features for voxel-based surface recognition","arxiv_id":"1701.04249","date":"2017-01-16","proceeding":null,"authors":["Dmitry Yarotsky"],"abstract":"We introduce a library of geometric voxel features for CAD surface\nrecognition/retrieval tasks. Our features include local versions of the\nintrinsic volumes (the usual 3D volume, surface area, integrated mean and\nGaussian curvature) and a few closely related quantities. We also compute Haar\nwavelet and statistical distribution features by aggregating raw voxel\nfeatures. We apply our features to object classification on the ESB data set\nand demonstrate accurate results with a small number of shallow decision trees.","url_abs":"http://arxiv.org/abs/1701.04249v1","url_pdf":"http://arxiv.org/pdf/1701.04249v1.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":"geometric-features-for-voxel-based-surface","repo_url":"https://github.com/yarotsky/voxelfeatures","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}