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3D object detection from multi-view images has drawn much attention over thepast few years. Existing methods mainly establish 3D representations frommulti-view images and adopt a dense detection head for object detection, oremploy object queries distributed in 3D space to localize objects. In thispaper, we design Multi-View 2D Objects guided 3D Object Detector (MV2D), whichcan lift any 2D object detector to multi-view 3D object detection. Since 2Ddetections can provide valuable priors for object existence, MV2D exploits 2Ddetectors to generate object queries conditioned on the rich image semantics.These dynamically generated queries help MV2D to recall objects in the field ofview and show a strong capability of localizing 3D objects. For the generatedqueries, we design a sparse cross attention module to force them to focus onthe features of specific objects, which suppresses interference from noises.The evaluation results on the nuScenes dataset demonstrate the dynamic objectqueries and sparse feature aggregation can promote 3D detection capability.MV2D also exhibits a state-of-the-art performance among existing methods. Wehope MV2D can serve as a new baseline for future research. Code is available at\url{https://github.com/tusen-ai/MV2D}.

Object as Query: Lifting any 2D Object Detector to 3D Detection