Recently, perception task based on Bird's-Eye View (BEV) representation has drawn more and more attention, and BEV representation is promising as the foundation for next-generation Autonomous Vehicle (AV) perception. However, most existing BEV solutions either require considerable resources to execute on-vehicle inference or suffer from modest performance. This paper proposes a simple yet effective framework, termed Fast-BEV , which is capable of performing faster BEV perception on the on-vehicle chips. Towards this goal, we first empirically find that the BEV representation can be sufficiently powerful without expensive transformer based transformation nor depth representation. Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference. We further introduce (4) a strong data augmentation strategy for both image and BEV space to avoid over-fitting, (5) a multi-frame feature fusion mechanism to leverage the temporal information. Through experiments, on 2080Ti platform, our R50 model can run 52.6 FPS with 47.3% NDS on the nuScenes validation set, exceeding the 41.3 FPS and 47.5% NDS of the BEVDepth-R50 model and 30.2 FPS and 45.7% NDS of the BEVDet4D-R50 model. Our largest model (R101@900x1600) establishes a competitive 53.5% NDS on the nuScenes validation set. We further develop a benchmark with considerable accuracy and efficiency on current popular on-vehicle chips. The code is released at: https://github.com/Sense-GVT/Fast-BEV.

翻译：近年来，基于鸟瞰图（BEV）表征的感知任务日益受到关注，BEV表征有望成为下一代自动驾驶汽车感知的基础。然而，现有大多数BEV解决方案要么需要大量资源在车载端推理，要么性能表现欠佳。本文提出一种简洁高效的框架，命名为Fast-BEV，能够实现在车载芯片上进行更快速的BEV感知。为实现该目标，我们首先通过实验发现，无需昂贵的Transformer变换或深度表征，BEV表征仍可保持足够强大的性能。Fast-BEV由五部分组成，我们创新性提出：（1）一种轻量级且易于部署的视图变换方法，可快速将2D图像特征转换至3D体素空间；（2）一种多尺度图像编码器，利用多尺度信息提升性能；（3）一种高效BEV编码器，专为加速车载推理而设计。此外，我们进一步引入（4）针对图像和BEV空间的强数据增强策略以避免过拟合；（5）一种多帧特征融合机制以利用时序信息。实验结果表明，在2080Ti平台上，我们的R50模型在nuScenes验证集上以47.3% NDS达到52.6 FPS，超出BEVDepth-R50模型的41.3 FPS和47.5% NDS，以及BEVDet4D-R50模型的30.2 FPS和45.7% NDS。我们最大的模型（R101@900x1600）在nuScenes验证集上取得具有竞争力的53.5% NDS。此外，我们还在当前主流车载芯片上构建了兼具高精度与高效率的基准测试。代码已开源于：https://github.com/Sense-GVT/Fast-BEV。