Visual bird’s eye view (BEV) perception, dute to its excellent perceptual capabilities, is progressively replacing costly LiDAR-based perception systems, especially in the realm of urban intelligent driving. However, this type of perception still relies on LiDAR data to construct ground truth databases, a process that is both cumbersome and time-consuming. Additionally, most mass-produced autonomous driving systems are equipped solely with surround camera sensors and lack the LiDAR data necessary for precise annotation. To tackle this challenge, we propose a fine-tuning method for BEV perception network based on visual 2D semantic perception, aimed at enhancing the model’s generalization capabilities in new scene data. Leveraging the maturity of 2D perception technologies, our method utilizes only 2D semantic segmentation labels and monocular depth estimations, thereby significantly reducing the dependence on expensive BEV ground truths and offering strong potential for industrial deployment. Extensive experiments and comparative analyses on the nuScenes and Waymo datasets demonstrate the effectiveness of our method. Specifically, it improves mAP and NDS by 2.51% and 1.93% on nuScenes, and by 1.21% and 0.78% on Waymo, respectively, validating its practical utility and robustness across diverse domains.