  Semantic segmentation in bird's eye view (BEV) is an important task for
autonomous driving. Though this task has attracted a large amount of research
efforts, it is still challenging to flexibly cope with arbitrary (single or
multiple) camera sensors equipped on the autonomous vehicle. In this paper, we
present BEVSegFormer, an effective transformer-based method for BEV semantic
segmentation from arbitrary camera rigs. Specifically, our method first encodes
image features from arbitrary cameras with a shared backbone. These image
features are then enhanced by a deformable transformer-based encoder. Moreover,
we introduce a BEV transformer decoder module to parse BEV semantic
segmentation results. An efficient multi-camera deformable attention unit is
designed to carry out the BEV-to-image view transformation. Finally, the
queries are reshaped according the layout of grids in the BEV, and upsampled to
produce the semantic segmentation result in a supervised manner. We evaluate
the proposed algorithm on the public nuScenes dataset and a self-collected
dataset. Experimental results show that our method achieves promising
performance on BEV semantic segmentation from arbitrary camera rigs. We also
demonstrate the effectiveness of each component via ablation study.
