Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12366)


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 applications. 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-measure.


3D lane detection Geometry-guided anchor Two-stage framework Monocular camera Unified network 



This work was supported by Apollo autonomous driving solution, Baidu USA.

Supplementary material

504479_1_En_40_MOESM1_ESM.pdf (22.2 mb)
Supplementary material 1 (pdf 22709 KB)


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Baidu ApolloSunnyvaleUSA

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