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Learning powerful representations in bird's-eye-view (BEV) for perceptiontasks is trending and drawing extensive attention both from industry andacademia. Conventional approaches for most autonomous driving algorithmsperform detection, segmentation, tracking, etc., in a front or perspectiveview. As sensor configurations get more complex, integrating multi-sourceinformation from different sensors and representing features in a unified viewcome of vital importance. BEV perception inherits several advantages, asrepresenting surrounding scenes in BEV is intuitive and fusion-friendly; andrepresenting objects in BEV is most desirable for subsequent modules as inplanning and/or control. The core problems for BEV perception lie in (a) how toreconstruct the lost 3D information via view transformation from perspectiveview to BEV; (b) how to acquire ground truth annotations in BEV grid; (c) howto formulate the pipeline to incorporate features from different sources andviews; and (d) how to adapt and generalize algorithms as sensor configurationsvary across different scenarios. In this survey, we review the most recentworks on BEV perception and provide an in-depth analysis of differentsolutions. Moreover, several systematic designs of BEV approach from theindustry are depicted as well. Furthermore, we introduce a full suite ofpractical guidebook to improve the performance of BEV perception tasks,including camera, LiDAR and fusion inputs. At last, we point out the futureresearch directions in this area. We hope this report will shed some light onthe community and encourage more research effort on BEV perception. We keep anactive repository to collect the most recent work and provide a toolbox for bagof tricks at https://github.com/OpenDriveLab/Birds-eye-view-Perception

Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe