2020/01/01 by Yuliang Guo, Guang Chen, Peitao Zhao +4 · 114 citations
Computer Science · Engineering · Environmental Science · #Advanced Neural Network Applications #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #Computer vision #Encoding (memory) #Frame (networking) #Geometric transformation #Image (mathematics) #Pattern recognition (psychology) #Remote Sensing and LiDAR Applications #Scalability #Segmentation #Subnetwork #Transformation (genetics) #cs.CV
paper · pdf · doi:10.1007/978-3-030-58589-1_40
published in Lecture notes in computer science, 666-681 (Springer Science+Business Media)
openalex publication_date 2020/01/01 · arxiv created 2020/03/24 · arxiv updated 2020/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.