BEVFormer
BEVFormer is an official implementation of a cutting-edge, camera-only framework for autonomous driving perception, published at ECCV 2022. It unifies Bird's-Eye-View representation learning for multiple tasks, including 3D object detection and semantic map segmentation, using spatiotemporal transformers. The architecture employs predefined grid-shaped BEV queries to integrate information across time and space. Spatial cross-attention layers extract features from specific regions across multiple camera views, while temporal self-attention layers recurrently fuse historical BEV data to enhance tracking and robustness. This approach achieves state-of-the-art results on the nuScenes benchmark, reaching 56.9% NDS on the camera-only detection task, a significant improvement that rivals LiDAR-based systems. An optimized variant known as BEVFormer++ secured first place on the Waymo Open Dataset 3D Camera-Only Detection Challenge. The framework supports various model configurations, including lightweight versions wit