TinyBEV: Compact Temporal Fusion for Multi-View 3D Perception

Multi-view camera-based 3D object detection through unified Bird's Eye View (BEV) representation has become popular for autonomous driving due to its low cost, but efficiently inferring precise spatial and temporal information from cameras alone remains a significant challenge. Transformer-based approaches have shown substantial performance improvements but have the drawback of quadratic memory complexity — making these architectures ill-suited for edge deployment. Recently, State Space Models (SSMs) offer a more favorable balance of computational efficiency and performance in 2D vision, suggesting that they could help here as well. We present TinyBEV, an efficient BEV framework for multi-view 3D perception. For spatial modeling, we replace cross attention with SSMs that fusing BEV and camera images with linear complexity. For temporal modeling, we adopt a lightweight, linear-complexity history-fusion scheme that uses explicit time conditioning and channel-level aggregation instead of cross-frame attention. Both fusion strategies follow small constant scaling with respect to history length and enabling edge-friendly deployment. Experiments on NuScenes datasets demonstrate that TinyBEV is comparable with other state-of-the-art methods across diverse visual perception metrics with advantages in computational efficiency.