{"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/line-as-object-datasets-and-framework-for","title":"Sem-LSD: A Learning-based Semantic Line Segment Detector","arxiv_id":"1909.06591","date":"2019-09-14","proceeding":null,"authors":["Yi Sun","Xushen Han","Kai Sun","Boren Li","Yongjiang Chen","Mingyang Li"],"abstract":"In this paper, we introduces a new type of line-shaped image representation, named semantic line segment (Sem-LS) and focus on solving its detection problem. Sem-LS contains high-level semantics and is a compact scene representation where only visually salient line segments with stable semantics are preserved. Combined with high-level semantics, Sem-LS is more robust under cluttered environment compared with existing line-shaped representations. The compactness of Sem-LS facilitates its use in large-scale applications, such as city-scale SLAM (simultaneously localization and mapping) and LCD (loop closure detection). Sem-LS detection is a challenging task due to its significantly different appearance from existing learning-based image representations such as wireframes and objects. For further investigation, we first label Sem-LS on two well-known datasets, KITTI and KAIST URBAN, as new benchmarks. Then, we propose a learning-based Sem-LS detector (Sem-LSD) and devise new module as well as metrics to address unique challenges in Sem-LS detection. Experimental results have shown both the efficacy and efficiency of Sem-LSD. Finally, the effectiveness of the proposed Sem-LS is supported by two experiments on detector repeatability and a city-scale LCD problem. Labeled datasets and code will be released shortly.","url_abs":"https://arxiv.org/abs/1909.06591v2","url_pdf":"https://arxiv.org/pdf/1909.06591v2.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":"line-as-object-datasets-and-framework-for","repo_url":"https://github.com/SunLoveSheep/Sem-LSD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"line-segment-detection","task_name":"Line Segment Detection"},{"task_slug":"loop-closure-detection","task_name":"Loop Closure Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1909.06591","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}