  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 CRF-modulated depth estimation module enforcing object-level
consistencies, a long-term temporal aggregation module with extended receptive
fields, and a two-stage object decoder combining perspective techniques with
CRF-modulated depth embedding. These enhancements lead to a "modernized" dense
BEV framework dubbed BEVNeXt. On the nuScenes benchmark, BEVNeXt outperforms
both BEV-based and query-based frameworks under various settings, achieving a
state-of-the-art result of 64.2 NDS on the nuScenes test set. Code will be
available at \url{https://github.com/woxihuanjiangguo/BEVNeXt}.
