{"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-segment-detection-using-transformers","title":"Line Segment Detection Using Transformers without Edges","arxiv_id":"2101.01909","date":"2021-01-06","proceeding":"CVPR 2021 1","authors":["Yifan Xu","Weijian Xu","David Cheung","Zhuowen Tu"],"abstract":"In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LETR), takes advantages of having integrated tokenized queries, a self-attention mechanism, and an encoding-decoding strategy within Transformers by skipping standard heuristic designs for the edge element detection and perceptual grouping processes. We equip Transformers with a multi-scale encoder/decoder strategy to perform fine-grained line segment detection under a direct endpoint distance loss. This loss term is particularly suitable for detecting geometric structures such as line segments that are not conveniently represented by the standard bounding box representations. The Transformers learn to gradually refine line segments through layers of self-attention. In our experiments, we show state-of-the-art results on Wireframe and YorkUrban benchmarks.","url_abs":"https://arxiv.org/abs/2101.01909v2","url_pdf":"https://arxiv.org/pdf/2101.01909v2.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-segment-detection-using-transformers","repo_url":"https://github.com/mlpc-ucsd/LETR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"line-segment-detection-using-transformers","repo_url":"https://github.com/abrarum/bezierobjdet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"line-segment-detection","task_name":"Line Segment Detection"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/line-segment-detection-on-york-urban-dataset","task":"Line Segment Detection","dataset":"York Urban Dataset","model":"LETR","rank_in_archive_order":14,"of":16,"metrics":{"FH":"66.9","sAP10":"29.4","sAP15":"31.7"},"uses_additional_data":true},{"leaderboard":"/sota/multi-task-learning-on-wireframe-dataset","task":"Multi-Task Learning","dataset":"wireframe dataset","model":"LETR","rank_in_archive_order":1,"of":1,"metrics":{"FH":"83.3","sAP10":"65.2","sAP15":"67.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2101.01909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.01909"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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