OneBEV：使用单张全景图像进行鸟瞰图语义建图
OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping
AI总结：
本文提出OneBEV，仅用单张全景图像进行鸟瞰图语义建图，设计MVT模块处理畸变并转换特征，贡献两个数据集，在nuScenes-360和DeepAccident-360上达到SOTA。
摘要：
在自动驾驶领域，鸟瞰图（BEV）感知因其能提供比针孔前视图图像和全景图更全面的信息而日益受到关注。传统BEV方法依赖多个窄视场相机和复杂的位姿估计，常面临标定与同步问题。为突破上述挑战，本文提出OneBEV，一种仅使用单张全景图像作为输入的新型BEV语义建图方法，简化了建图流程并降低了计算复杂度。我们专门设计了名为Mamba View Transformation（MVT）的畸变感知模块，用于处理全景图中的空间畸变，在不依赖传统注意力机制的情况下将前视图特征转换为BEV特征。除高效框架外，我们还贡献了两个为OneBEV任务定制的数据集：nuScenes-360和DeepAccident-360。实验结果表明，OneBEV在nuScenes-360和DeepAccident-360上分别达到51.1%和36.1%的mIoU，实现了最先进的性能。本工作推动了自动驾驶中的BEV语义建图研究，为更先进可靠的自动驾驶系统铺平了道路。
英文摘要：
In the field of autonomous driving, Bird's-Eye-View (BEV) perception has attracted increasing attention in the community since it provides more comprehensive information compared with pinhole front-view images and panoramas. Traditional BEV methods, which rely on multiple narrow-field cameras and complex pose estimations, often face calibration and synchronization issues. To break the wall of the aforementioned challenges, in this work, we introduce OneBEV, a novel BEV semantic mapping approach using merely a single panoramic image as input, simplifying the mapping process and reducing computational complexities. A distortion-aware module termed Mamba View Transformation (MVT) is specifically designed to handle the spatial distortions in panoramas, transforming front-view features into BEV features without leveraging traditional attention mechanisms. Apart from the efficient framework, we contribute two datasets, i.e., nuScenes-360 and DeepAccident-360, tailored for the OneBEV task. Experimental results showcase that OneBEV achieves state-of-the-art performance with 51.1% and 36.1% mIoU on nuScenes-360 and DeepAccident-360, respectively. This work advances BEV semantic mapping in autonomous driving, paving the way for more advanced and reliable autonomous systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
