{"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/floor-plan-image-segmentation-via-scribble","title":"Floor Plan Image Segmentation Via Scribble-Based Semi-Weakly Supervised Learning: A Style and Category-Agnostic Approach","arxiv_id":null,"date":"2024-02-15","proceeding":"N.A. 2024 2","authors":["Jielin CHEN;Rudi STOUFFS"],"abstract":"The field of architectural design is experiencing a transformative shift towards the integration of advanced computational methodologies, aiming to revolutionize traditional practices through automation. A pivotal aspect is the automation of floor plan recognition. This task faces challenges due to varied floor plan styles and the need for large-scale annotated datasets for learning-based methods, hindered by the lack of standardized visualization rules and specialized annotation knowledge. Our study introduces a novel scribble-based semi-weakly-supervised framework, merging weakly annotated and unlabeled images to boost model robustness and generalizability. This framework benefits from a simplified annotation process while retaining detailed information. Accordingly, we provide a new benchmark dataset for floor plan image parsing covering a wide range of architectural styles and categories. Experiments with our proposed framework demonstrate marked improvements in parsing accuracy and model adaptability, significantly surpassing current state-of-the-art solutions.","url_abs":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4727643","url_pdf":"http://dx.doi.org/10.2139/ssrn.4727643","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":"floor-plan-image-segmentation-via-scribble","repo_url":"https://github.com/JanineCHEN/FP4S","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-semantic-segmentation","task_name":"Semi-Supervised Semantic Segmentation"},{"task_slug":"weakly-supervised-segmentation","task_name":"Weakly supervised segmentation"},{"task_slug":"weakly-supervised-learning","task_name":"Weakly-supervised Learning"}],"methods":[],"datasets_introduced":[{"slug":"fp4s","name":"FP4S","full_name":"Floor plan image segmentation via scribble-based semi-weakly-supervised learning"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-fp4s","task":"Semantic Segmentation","dataset":"FP4S","model":"FP4S","rank_in_archive_order":1,"of":1,"metrics":{"Dice (Average)":"0.34"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}