DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection

• URL: http://arxiv.org/abs/2505.13266v1
• Date: Mon, 19 May 2025 15:47:20 GMT
• Title: DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection
• Authors: Yehao Liu, Xiaosu Xu, Zijian Wang, Yiqing Yao,
• Abstract summary: 3D Lane detection plays an important role in autonomous driving.<n>Recent advances primarily build Birds-Eye-View (BEV) feature from front-view (FV) images to perceive 3D information of Lane more effectively.<n>However, constructing accurate BEV information from FV image is limited due to the lacking of depth information.
• Score: 3.7115000857388494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Lane detection plays an important role in autonomous driving. Recent advances primarily build Birds-Eye-View (BEV) feature from front-view (FV) images to perceive 3D information of Lane more effectively. However, constructing accurate BEV information from FV image is limited due to the lacking of depth information, causing previous works often rely heavily on the assumption of a flat ground plane. Leveraging monocular depth estimation to assist in constructing BEV features is less constrained, but existing methods struggle to effectively integrate the two tasks. To address the above issue, in this paper, an accurate 3D lane detection method based on depth-aware BEV feature transtormation is proposed. In detail, an effective feature extraction module is designed, in which a Depth Net is integrated to obtain the vital depth information for 3D perception, thereby simplifying the complexity of view transformation. Subquently a feature reduce module is proposed to reduce height dimension of FV features and depth features, thereby enables effective fusion of crucial FV features and depth features. Then a fusion module is designed to build BEV feature from prime FV feature and depth information. The proposed method performs comparably with state-of-the-art methods on both synthetic Apollo, realistic OpenLane datasets.

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