  Sparse query-based paradigms have achieved significant success in multi-view
3D detection for autonomous vehicles. Current research faces challenges in
balancing between enlarging receptive fields and reducing interference when
aggregating multi-view features. Moreover, different poses of cameras present
challenges in training global attention models. To address these problems, this
paper proposes a divided view method, in which features are modeled globally
via the visibility crossattention mechanism, but interact only with partial
features in a divided local virtual space. This effectively reduces
interference from other irrelevant features and alleviates the training
difficulties of the transformer by decoupling the position embedding from
camera poses. Additionally, 2D historical RoI features are incorporated into
the object-centric temporal modeling to utilize highlevel visual semantic
information. The model is trained using a one-to-many assignment strategy to
facilitate stability. Our framework, named DVPE, achieves state-of-the-art
performance (57.2% mAP and 64.5% NDS) on the nuScenes test set. Codes will be
available at https://github.com/dop0/DVPE.
