Learning To Detect 3D Lanes by Shape Matching and Embedding

Ruixin Liu, Zhihao Guan, Zejian Yuan, Ao Liu, Tong Zhou, Tang Kun, Erlong Li, Chao Zheng, Shuqi Mei; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 4291-4299

3D lane detection based on LiDAR point clouds is a challenging task that requires precise locations, accurate topologies, and distinguishable instances. In this paper, we propose a dual-level shape attention network (DSANet) with two branches for high-precision 3D lane predictions. Specifically, one branch predicts the refined lane segment shapes and the shape embeddings that encode the approximate lane instance shapes, the other branch detects the coarse-grained structures of the lane instances. In the training stage, two-level shape matching loss functions are introduced to jointly optimize the shape parameters of the two-branch outputs, which are simple yet effective for precision enhancement. Furthermore, a shape-guided segments aggregator is proposed to help local lane segments aggregate into complete lane instances, according to the differences of instance shapes predicted at different levels. Experiments conducted on our BEV-3DLanes dataset demonstrate that our method outperforms previous methods.