{"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/gen-lanenet-a-generalized-and-scalable","title":"Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection","arxiv_id":"2003.10656","date":"2020-03-24","proceeding":"ECCV 2020 8","authors":["Yuliang Guo","Guang Chen","Peitao Zhao","Weide Zhang","Jinghao Miao","Jingao Wang","Tae Eun Choe"],"abstract":"We present a generalized and scalable method, called Gen-LaneNet, to detect 3D lanes from a single image. The method, inspired by the latest state-of-the-art 3D-LaneNet, is a unified framework solving image encoding, spatial transform of features and 3D lane prediction in a single network. However, we propose unique designs for Gen-LaneNet in two folds. First, we introduce a new geometry-guided lane anchor representation in a new coordinate frame and apply a specific geometric transformation to directly calculate real 3D lane points from the network output. We demonstrate that aligning the lane points with the underlying top-view features in the new coordinate frame is critical towards a generalized method in handling unfamiliar scenes. Second, we present a scalable two-stage framework that decouples the learning of image segmentation subnetwork and geometry encoding subnetwork. Compared to 3D-LaneNet, the proposed Gen-LaneNet drastically reduces the amount of 3D lane labels required to achieve a robust solution in real-world application. Moreover, we release a new synthetic dataset and its construction strategy to encourage the development and evaluation of 3D lane detection methods. In experiments, we conduct extensive ablation study to substantiate the proposed Gen-LaneNet significantly outperforms 3D-LaneNet in average precision(AP) and F-score.","url_abs":"https://arxiv.org/abs/2003.10656v1","url_pdf":"https://arxiv.org/pdf/2003.10656v1.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":"gen-lanenet-a-generalized-and-scalable","repo_url":"https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-lane-detection","task_name":"3D Lane Detection"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"lane-detection","task_name":"Lane Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"3d-lane-synthetic-dataset","name":"3D Lane Synthetic Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-lane-detection-on-apollo-synthetic-3d-lane","task":"3D Lane Detection","dataset":"Apollo Synthetic 3D Lane","model":"Gen-LaneNet","rank_in_archive_order":9,"of":10,"metrics":{"F1":"88.1","X error far":"0.496","X error near":"0.061","Z error far":"0.214","Z error near":"0.012"},"uses_additional_data":false},{"leaderboard":"/sota/3d-lane-detection-on-openlane","task":"3D Lane Detection","dataset":"OpenLane","model":"Gen-LaneNet","rank_in_archive_order":21,"of":21,"metrics":{"Curve":"33.5","Extreme Weather":"28.1","F1 (all)":"32.3","FPS (pytorch)":"-","Intersection":"21.4","Merge & Split":"31.0","Night":"18.7","Up & Down":"25.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.10656","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}