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 https://github.com/ChenhongyiYang/WidthFormer .

翻译：本文提出WidthFormer，一种新型基于Transformer的鸟瞰视角（BEV）3D检测方法，专为实时自动驾驶应用设计。WidthFormer兼具计算高效性、鲁棒性，且无需特殊工程部署。我们提出一种新型3D位置编码机制，能够精确封装3D几何信息，使模型仅需单层Transformer解码器即可生成高质量BEV表征。该机制对现有稀疏3D目标检测器亦有助益。受近期研究工作启发，我们通过垂直压缩图像特征作为注意力机制的键与值，进一步提升模型效率。同时引入两个模块以补偿特征压缩可能造成的信息损失。在广泛使用的nuScenes 3D目标检测基准上的实验表明，本方法在不同3D检测架构中均优于先前方法。更重要的是，模型具有极高效率：例如，在使用$256\times 704$输入图像时，在NVIDIA 3090 GPU上可实现1.5毫秒延迟。此外，WidthFormer对不同程度的相机扰动表现出强鲁棒性。本研究为在真实复杂道路环境中部署BEV转换方法提供了重要见解。代码开源于https://github.com/ChenhongyiYang/WidthFormer。