{"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/latr-3d-lane-detection-from-monocular-images","title":"LATR: 3D Lane Detection from Monocular Images with Transformer","arxiv_id":"2308.04583","date":"2023-08-08","proceeding":"ICCV 2023 1","authors":["Yueru Luo","Chaoda Zheng","Xu Yan","Tang Kun","Chao Zheng","Shuguang Cui","Zhen Li"],"abstract":"3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances primarily rely on structural 3D surrogates (e.g., bird's eye view) built from front-view image features and camera parameters. However, the depth ambiguity in monocular images inevitably causes misalignment between the constructed surrogate feature map and the original image, posing a great challenge for accurate lane detection. To address the above issue, we present a novel LATR model, an end-to-end 3D lane detector that uses 3D-aware front-view features without transformed view representation. Specifically, LATR detects 3D lanes via cross-attention based on query and key-value pairs, constructed using our lane-aware query generator and dynamic 3D ground positional embedding. On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance lane information. On the other hand, 3D space information is injected as positional embedding from an iteratively-updated 3D ground plane. LATR outperforms previous state-of-the-art methods on both synthetic Apollo, realistic OpenLane and ONCE-3DLanes by large margins (e.g., 11.4 gain in terms of F1 score on OpenLane). Code will be released at https://github.com/JMoonr/LATR .","url_abs":"https://arxiv.org/abs/2308.04583v2","url_pdf":"https://arxiv.org/pdf/2308.04583v2.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":"latr-3d-lane-detection-from-monocular-images","repo_url":"https://github.com/jmoonr/latr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-lane-detection","task_name":"3D Lane Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"lane-detection","task_name":"Lane Detection"}],"methods":[{"method_slug":"apollo","method_name":"Apollo"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-lane-detection-on-openlane","task":"3D Lane Detection","dataset":"OpenLane","model":"LATR","rank_in_archive_order":5,"of":21,"metrics":{"Curve":"68.2","Extreme Weather":"57.1","F1 (all)":"61.9","Intersection":"52.3","Merge & Split":"61.5","Night":"55.4","Up & Down":"55.2"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2308.04583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.04583"}},"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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