CAMAv2: A Vision-Centric Approach for Static Map Element Annotation

The recent advancement of Bird’s Eye View (BEV) perception algorithms requires extensive, high-quality annotated map data for effective training and deployment in real-world scenarios. However, existing HD map-based auto-labeling methods, like those found in public datasets, face significant issues related to efficiency and accuracy. For instance, the nuScenes dataset reveals considerable misalignment and inconsistency between images and their annotations, with an average reprojection error of approximately 8.03 pixels. Additionally, the dependence on HD maps limits the applicability of these methods for large-scale, real-world auto-labeling. To tackle these challenges, we introduce CAMAv2: a vision-centric approach for Consistent and Accurate Map Annotation. This pipeline primarily utilizes camera inputs to generate precise 3D annotations of static map elements, achieving high reprojection accuracy across all surrounding cameras and maintaining spatiotemporal consistency throughout the entire sequence. Importantly, CAMAv2 annotations show lower reprojection errors compared to the original nuScenes map elements, with errors of 4.96 pixels versus 8.03 pixels. Comprehensive evaluations across various public datasets confirm the feasibility and generalizability of our pipeline in diverse environments, as well as under different weather and lighting conditions.