In this work, we present WidthFormer, a novel transformer-based
Bird's-Eye-View (BEV) 3D detection method tailored for real-time
autonomous-driving applications. WidthFormer is computationally efficient,
robust and does not require any special engineering effort to deploy. In this
work, we propose a novel 3D positional encoding mechanism capable of accurately
encapsulating 3D geometric information, which enables our model to generate
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. We also introduce 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 latency on NVIDIA
3090 GPU. 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
this https URL .

通过提出的 WidthFormer 模型，本研究将 Bird's-Eye-View（BEV）3D 检测方法应用于实时自动驾驶应用，其具有计算效率高、有效、无需特殊工程部署等特点，并通过引入 3D 位置编码机制实现了准确融合 3D 几何信息，从而实现生成高质量 BEV 表示的目标。