True Match: Leveraging 2D-Assisted Queries for Multi-view 3D Detection in Polar Space

Sparse Query-Based paradigms for multi-view 3D object detection achieved remarkable success, but the redundant predictions still affects the detection accuracy and object localization because of the following shortcomings: 1) 3D position embedding exhibits weak spatial perception ability and cannot capture subtle differences between similar objects. 2) Randomly initialized object queries lack prior knowledge of reference points and object information, which hinders their accuracy in matching with corresponding objects. 3) The temporal fusion process fails to effectively capture object details, making it challenging to localize object accurately. To address these issues, we propose an innovative framework T-Match, which leverages prior knowledge from a 2D detector to initialize object queries. Based on Polar space, both 2D and 3D information are integrated to comprehensively capture object details, object queries iteratively updates through Cross-Domain Spatio-Temporal Attention, which incorporates cross-domain object information, and Polar-Aware Cross Attention, which aggregates image features fused with Polar position embedding, refining matching results while reducing redundant predictions. T-Match achieves state-of-the-art performance on nuScenes Test with 57.5% mAP and 64.9% NDS, exceeding multi-modal frameworks that combine image and radar data.