Bi²Lane: Bi-Directional Temporal Refinement with Bi-Level Feature Aggregation for 3D Lane Detection

Monocular 3D lane detection has recently received increasing research attention in autonomous driving due to its application effectiveness and simplicity. However, depending solely on the limited semantic information from a single image makes current monocular detection methods unable to deal with complex scenarios, such as occluded, blurred, and unaligned scenes. In this study, we introduce an end-to-end framework named Bi²Lane which models temporal dependency in a continuous sequence. It recurrently utilizes detected lanes within historical frames as prior information to achieve robust lane detection. Additionally, Bi²Lane employs temporal reverse refinement together with temporal forward refinement to achieve bi-directional temporal refinement (BDTR) while maintaining a robust temporal dependency. For the refined features of different frames, we design a bi-level feature aggregation module (BLFA) to fuse them in both point-level and line-level manners, enabling a comprehensive feature representation to deal with complicated road scenes. Extensive experiments conducted on the OpenLane dataset demonstrate the superiority of Bi²Lane, achieving a notable F1 score of 63.8% using a simple ResNet50 backbone, surpassing the performance of existing state-of-the-art methods.