BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection

arXiv:2312.01696v2 Announce Type: replace
Abstract: Recently, the rise of query-based Transformer decoders is reshaping camera-based 3D object detection. These query-based decoders are surpassing the traditional dense BEV (Bird's Eye View)-based methods. However, we argue that dense BEV frameworks remain important due to their outstanding abilities in depth estimation and object localization, depicting 3D scenes accurately and comprehensively. This paper aims to address the drawbacks of the existing dense BEV-based 3D object detectors by introducing our proposed enhanced components, including a …

3d object 3d object detection arxiv cs.cv detection frameworks object type