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3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation

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arxiv 2011.01535 v2 pith:T473ESIX submitted 2020-11-01 cs.CV

Netalee Efrat , Max Bluvstein , Shaul Oron , Dan Levi , Noa Garnett , Bat El Shlomo This is my paper · ORCID
classification cs.CV
keywords laned-lanenetlanesanchordetectionfreerepresentationoriginal
verification ladder T0 review T1 audit T2 compute T3 formal
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3D-LaneNet+ is a camera-based DNN method for anchor free 3D lane detection which is able to detect 3d lanes of any arbitrary topology such as splits, merges, as well as short and perpendicular lanes. We follow recently proposed 3D-LaneNet, and extend it to enable the detection of these previously unsupported lane topologies. Our output representation is an anchor free, semi-local tile representation that breaks down lanes into simple lane segments whose parameters can be learnt. In addition we learn, per lane instance, feature embedding that reasons for the global connectivity of locally detected segments to form full 3d lanes. This combination allows 3D-LaneNet+ to avoid using lane anchors, non-maximum suppression, and lane model fitting as in the original 3D-LaneNet. We demonstrate the efficacy of 3D-LaneNet+ using both synthetic and real world data. Results show significant improvement relative to the original 3D-LaneNet that can be attributed to better generalization to complex lane topologies, curvatures and surface geometries.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. 3D Lane Detection with Odometry for High-Speed Vehicle Racing

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Fusing multi-camera lane predictions with odometry pre-integration improves 3D lane detection on a new racing dataset, reaching F1 > 0.9 at nearly 300 Hz.

  2. Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression

    cs.CV 2024-12 conditional novelty 6.0 of 10

    Anchor3DLane++ predicts 3D lanes from front-view features using sample-adaptive sparse 3D anchors, improving F1 scores on OpenLane, ApolloSim, and ONCE-3DLanes beyond prior methods.

  3. Monocular Lane Detection Based on Deep Learning: A Survey

    cs.CV 2024-11 conditional novelty 4.0 of 10

    A structured review of 2D and 3D monocular lane detection methods, with a new four-axis taxonomy and unified FPS comparisons.

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