SGC-Lane: Monocular 3D Lane Detection with Standard-Definition Map Guidance and Lane Completion

Monocular 3D lane detection offers a cost-effective solutionfor autonomous driving perception but is inherently challenged by theill-posed problem of inferring 3D geometry from a 2D image. Standard-Definition (SD) maps offer a lightweight prior for road topology, promis-ing enhanced accuracy for distant or occluded lanes. However, their in-herent inaccuracies and representation as road-level centerlines, whichmisalign with the actual lanes to be perceived, pose significant challengesfor direct integration. To tackle this, we propose SGC-Lane, a novelframework that advances monocular 3D lane detection through SD mapguidance and lane completion. Specifically, we propose an FV-GuidedMap Encoder that generates a lane probability map and structured mapfeatures, where the feature channels are aligned with potential lane in-stances. This probability map directs attention toward lane-specific re-gions in the front view (FV), refining image features and initializing lanequeries by the Local Spatial Cross-Attention module for precise geome-try decoding. In addition, to mitigate short-range truncation, we designa Step Consistency Completion (SCC) Head that is used to predict andcluster lane endpoints, effectively recovering missing segments to producecomplete 3D lane representations. On the large-scale OpenLane dataset,our method achieves a 65.6% F1-score, showing improved performanceagainst existing approaches with the assistance of only coarse road-levelSD map priors. The improvements are especially evident in challengingconditions, such as roads with sharp curves and complex intersections.Code is released at https://github.com/FuqingJIang/SGC-Lane.