{"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/review-of-statistical-shape-spaces-for-3d","title":"Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces","arxiv_id":"1209.6491","date":"2012-09-28","proceeding":null,"authors":["Alan Brunton","Augusto Salazar","Timo Bolkart","Stefanie Wuhrer"],"abstract":"With systems for acquiring 3D surface data being evermore commonplace, it has\nbecome important to reliably extract specific shapes from the acquired data. In\nthe presence of noise and occlusions, this can be done through the use of\nstatistical shape models, which are learned from databases of clean examples of\nthe shape in question. In this paper, we review, analyze and compare different\nstatistical models: from those that analyze the variation in geometry globally\nto those that analyze the variation in geometry locally. We first review how\ndifferent types of models have been used in the literature, then proceed to\ndefine the models and analyze them theoretically, in terms of both their\nstatistical and computational aspects. We then perform extensive experimental\ncomparison on the task of model fitting, and give intuition about which type of\nmodel is better for a few applications. Due to the wide availability of\ndatabases of high-quality data, we use the human face as the specific shape we\nwish to extract from corrupted data.","url_abs":"http://arxiv.org/abs/1209.6491v3","url_pdf":"http://arxiv.org/pdf/1209.6491v3.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":"review-of-statistical-shape-spaces-for-3d","repo_url":"https://github.com/TimoBolkart/GlobalLocalFaceModels","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1209.6491","atlas_url":"https://app.syntology.ai/?focus=1209.6491","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}