深入探究鸟瞰感知的“魔鬼”：综述、评估与配方
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
AI总结：
本文综述自动驾驶BEV感知的视图变换、标注、多源融合与传感器泛化等关键问题，分析最新方法和工业方案，并给出面向相机、LiDAR及融合输入的性能提升指南。
摘要：
为感知任务学习强大的鸟瞰视图（bird's-eye-view，BEV）表征正成为趋势，并引起工业界和学术界的广泛关注。大多数自动驾驶算法的传统方法在前视图或透视图中执行检测、分割、跟踪等任务。随着传感器配置变得更加复杂，整合来自不同传感器的多源信息并在统一视图中表征特征变得至关重要。BEV感知继承了若干优势：在BEV中表征周围场景直观且便于融合；在BEV中表征物体也最适合规划和/或控制等后续模块。BEV感知的核心问题在于：(a)如何通过从透视图到BEV的视图变换重建丢失的3D信息；(b)如何获取BEV网格中的真值标注；(c)如何构建流水线以整合来自不同来源和视图的特征；以及(d)当不同场景中的传感器配置发生变化时，如何使算法适应并泛化。在本综述中，我们回顾了关于BEV感知的最新工作，并对不同解决方案进行深入分析。此外，还描述了工业界若干系统化的BEV方法设计。进一步地，我们介绍了一整套实用指南，以提升BEV感知任务的性能，包括相机、LiDAR和融合输入。最后，我们指出该领域未来的研究方向。我们希望本报告能为社区带来启发，并鼓励更多关于BEV感知的研究。我们维护一个活跃的代码仓库以收集最新工作，并在 https://github.com/OpenDriveLab/Birds-eye-view-Perception 提供包含技巧集合的工具箱。
英文摘要：
Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches for most autonomous driving algorithms perform detection, segmentation, tracking, etc., in a front or perspective view. As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance. BEV perception inherits several advantages, as representing surrounding scenes in BEV is intuitive and fusion-friendly; and representing objects in BEV is most desirable for subsequent modules as in planning and/or control. The core problems for BEV perception lie in (a) how to reconstruct the lost 3D information via view transformation from perspective view to BEV; (b) how to acquire ground truth annotations in BEV grid; (c) how to formulate the pipeline to incorporate features from different sources and views; and (d) how to adapt and generalize algorithms as sensor configurations vary across different scenarios. In this survey, we review the most recent works on BEV perception and provide an in-depth analysis of different solutions. Moreover, several systematic designs of BEV approach from the industry are depicted as well. Furthermore, we introduce a full suite of practical guidebook to improve the performance of BEV perception tasks, including camera, LiDAR and fusion inputs. At last, we point out the future research directions in this area. We hope this report will shed some light on the community and encourage more research effort on BEV perception. We keep an active repository to collect the most recent work and provide a toolbox for bag of tricks at https://github.com/OpenDriveLab/Birds-eye-view-Perception
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。
