{"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/partial-procedural-geometric-model-fitting","title":"Partial Procedural Geometric Model Fitting for Point Clouds","arxiv_id":"1610.04936","date":"2016-10-17","proceeding":null,"authors":["Zongliang Zhang","Jonathan Li","Yulan Guo","Yangbin Lin","Ming Cheng","Cheng Wang"],"abstract":"Geometric model fitting is a fundamental task in computer graphics and\ncomputer vision. However, most geometric model fitting methods are unable to\nfit an arbitrary geometric model (e.g. a surface with holes) to incomplete\ndata, due to that the similarity metrics used in these methods are unable to\nmeasure the rigid partial similarity between arbitrary models. This paper hence\nproposes a novel rigid geometric similarity metric, which is able to measure\nboth the full similarity and the partial similarity between arbitrary geometric\nmodels. The proposed metric enables us to perform partial procedural geometric\nmodel fitting (PPGMF). The task of PPGMF is to search a procedural geometric\nmodel space for the model rigidly similar to a query of non-complete point set.\nModels in the procedural model space are generated according to a set of\nparametric modeling rules. A typical query is a point cloud. PPGMF is very\nuseful as it can be used to fit arbitrary geometric models to non-complete\n(incomplete, over-complete or hybrid-complete) point cloud data. For example,\nmost laser scanning data is non-complete due to occlusion. Our PPGMF method\nuses Markov chain Monte Carlo technique to optimize the proposed similarity\nmetric over the model space. To accelerate the optimization process, the method\nalso employs a novel coarse-to-fine model dividing strategy to reject\ndissimilar models in advance. Our method has been demonstrated on a variety of\ngeometric models and non-complete data. Experimental results show that the\nPPGMF method based on the proposed metric is able to fit non-complete data,\nwhile the method based on other metrics is unable. It is also shown that our\nmethod can be accelerated by several times via early rejection.","url_abs":"http://arxiv.org/abs/1610.04936v1","url_pdf":"http://arxiv.org/pdf/1610.04936v1.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":"partial-procedural-geometric-model-fitting","repo_url":"https://github.com/Windfisch/papertool","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}