TAS-DAQ: Task-Adaptive Sparse Prediction With Dense Query Auxiliary Supervisory for Efficient 3D Object Detection

Detecting 3D objects from surround-view images focuses on capturing the spatio-temporal positions of the surrounding environment, serving as a pivotal capability for vision-centric autonomous driving and robotics. While existing approaches primarily employ either dense BEV queries or sparse 3D queries, both paradigms have inherent limitations: dense queries suffer from redundant feature interactions and optimization conflicts, while sparse queries rely on high-quality initialization and struggle with error propagation in complex scenarios. To address these challenges, we proposeTAS-DAQ, a novel two-stage framework that synergizes dense and sparse query strategies. In Stage I, we generate geometry-aware coarse queries through the BEV feature providing robust initialization, thereby ensuring robust query initialization with explicit 3D priors. Stage II introduces a learnable Query Bank with temporal fusion to iteratively refine sparse queries by capturing discriminative instance features across views and frames. Moreover, considering the optimization conflicts caused by redundant query interactions in dense paradigms, we introduce adaptive query aggregation in the query bank that dynamically prioritizes high-confidence queries from BEV features, effectively addressing query error propagation while enhancing instance-level representation consistency. Extensive experiments on the nuScenes R50 benchmark demonstrate state-of-the-art performance, achieving56.9 % NDSand46.1% mAP.