  Currently, detecting 3D objects in Bird's-Eye-View (BEV) is superior to other
3D detectors for autonomous driving and robotics. However, transforming image
features into BEV necessitates special operators to conduct feature sampling.
These operators are not supported on many edge devices, bringing extra
obstacles when deploying detectors. To address this problem, we revisit the
generation of BEV representation and propose detecting objects in perspective
BEV -- a new BEV representation that does not require feature sampling. We
demonstrate that perspective BEV features can likewise enjoy the benefits of
the BEV paradigm. Moreover, the perspective BEV improves detection performance
by addressing issues caused by feature sampling. We propose PersDet for
high-performance object detection in perspective BEV space based on this
discovery. While implementing a simple and memory-efficient structure, PersDet
outperforms existing state-of-the-art monocular methods on the nuScenes
benchmark, reaching 34.6% mAP and 40.8% NDS when using ResNet-50 as the
backbone.
