MamBEV: Enabling State Space Models to Learn Birds-Eye-View Representations
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
3D visual perception tasks, such as 3D detection from multi-camera images,
are essential components of autonomous driving and assistance systems. However,
designing computationally efficient methods remains a significant challenge. In
this paper, we propose a Mamba-based framework called MamBEV, which learns
unified Bird's Eye View (BEV) representations using linear spatio-temporal
SSM-based attention. This approach supports multiple 3D perception tasks with
significantly improved computational and memory efficiency. Furthermore, we
introduce SSM based cross-attention, analogous to standard cross attention,
where BEV query representations can interact with relevant image features.
Extensive experiments demonstrate MamBEV's promising performance across diverse
visual perception metrics, highlighting its advantages in input scaling
efficiency compared to existing benchmark models.
