Freq-3DLane: 3D Lane Detection From Monocular Images via Frequency-Aware Feature Fusion

3D lane detection provides richer spatial information than 2D lane detection planar position results. It improves vehicle perception in complex scenes, which is becoming increasingly important in intelligent driving. However, existing frameworks mainly focus on mapping front-view (FV) and bird’s-eye view (BEV) features and ignore the intrinsic correlations between different perspectives and scales. It can lead to incomplete feature extraction, affecting the perception accuracy of lane detection and adaptation ability to complex scenes. To alleviate these problems, we present a novel Freq-3DLane framework, an efficient end-to-end 3D lane detector. Instead of directly superimposing deeper and lower-level features, we propose a strategy for multi-scale information integration that exploits the frequency characteristics of features for image feature extraction. To enhance perception, we fuse image features at each scale through frequency processing to ensure that detailed information and global structure are fully utilized. Next, spatial transformation fusion captures the association between the FV and the BEV feature at any two-pixel position of both, thus enabling view feature transformation. In addition, attentional guidance enhances the lane semantic information to ensure recovery of the lane geometry for accurate 3D lane detection. Extensive results on two challenging benchmarks (Apollo 3D Lane Synthetic, and OpenLane) show that our model performs favorably against the state of the arts.