We introduce RoScenes, the largest multi-view roadside perception dataset, which aims to shed light on the development of vision-centric Bird's Eye View (BEV) approaches for more challenging traffic scenes. The highlights of RoScenes include significantly large perception area, full scene coverage and crowded traffic. More specifically, our dataset achieves surprising 21.13M 3D annotations within 64,000 $m^2$. To relieve the expensive costs of roadside 3D labeling, we present a novel BEV-to-3D joint annotation pipeline to efficiently collect such a large volume of data. After that, we organize a comprehensive study for current BEV methods on RoScenes in terms of effectiveness and efficiency. Tested methods suffer from the vast perception area and variation of sensor layout across scenes, resulting in performance levels falling below expectations. To this end, we propose RoBEV that incorporates feature-guided position embedding for effective 2D-3D feature assignment. With its help, our method outperforms state-of-the-art by a large margin without extra computational overhead on validation set. Our dataset and devkit will be made available at \url{https://github.com/xiaosu-zhu/RoScenes}.

翻译：我们推出RoScenes，这是目前规模最大的多视图路边感知数据集，旨在为更具挑战性的交通场景中基于视觉的中心鸟瞰图（BEV）方法的发展提供启示。该数据集的显著特点包括极大的感知范围、全场景覆盖以及密集交通。具体而言，我们的数据集在64,000平方米范围内实现了惊人的21.13M个三维标注。为缓解路边三维标注的高昂成本，我们提出了一种新颖的BEV到三维联合标注流水线，以高效收集如此大规模的数据。随后，我们对当前BEV方法在RoScenes上的有效性和效率进行了全面研究。被测方法因感知范围巨大及场景间传感器布局差异而表现不佳，性能水平低于预期。为此，我们提出RoBEV，它通过特征引导的位置嵌入实现了有效的二维-三维特征分配。借助这一方法，我们的模型在验证集上以无额外计算开销的优势大幅超越现有最优方法。数据集及开发工具包将在\url{https://github.com/xiaosu-zhu/RoScenes}上公开。