  We present WidthFormer, a novel transformer-based module to compute
Bird's-Eye-View (BEV) representations from multi-view cameras for real-time
autonomous-driving applications. WidthFormer is computationally efficient,
robust and does not require any special engineering effort to deploy. We first
introduce a novel 3D positional encoding mechanism capable of accurately
encapsulating 3D geometric information, which enables our model to compute
high-quality BEV representations with only a single transformer decoder layer.
This mechanism is also beneficial for existing sparse 3D object detectors.
Inspired by the recently proposed works, we further improve our model's
efficiency by vertically compressing the image features when serving as
attention keys and values, and then we develop two modules to compensate for
potential information loss due to feature compression. Experimental evaluation
on the widely-used nuScenes 3D object detection benchmark demonstrates that our
method outperforms previous approaches across different 3D detection
architectures. More importantly, our model is highly efficient. For example,
when using $256\times 704$ input images, it achieves 1.5 ms and 2.8 ms latency
on NVIDIA 3090 GPU and Horizon Journey-5 computation solutions. Furthermore,
WidthFormer also exhibits strong robustness to different degrees of camera
perturbations. Our study offers valuable insights into the deployment of BEV
transformation methods in real-world, complex road environments. Code is
available at https://github.com/ChenhongyiYang/WidthFormer .
