Sparse4D v3: Advancing End-to-End 3D Detection and Tracking
Xuewu Lin Zixiang Pei Tianwei Lin Lichao Huang Zhizhong Su
In autonomous driving perception systems, 3D detection and tracking are the
two fundamental tasks. This paper delves deeper into this field, building upon
the Sparse4D framework. We introduce two auxiliary training tasks (Temporal
Instance Denoising and Quality Estimation) and propose decoupled attention to
make structural improvements, leading to significant enhancements in detection
performance. Additionally, we extend the detector into a tracker using a
straightforward approach that assigns instance ID during inference, further
highlighting the advantages of query-based algorithms. Extensive experiments
conducted on the nuScenes benchmark validate the effectiveness of the proposed
improvements. With ResNet50 as the backbone, we witnessed enhancements of
3.0\%, 2.2\%, and 7.6\% in mAP, NDS, and AMOTA, achieving 46.9\%, 56.1\%, and
49.0\%, respectively. Our best model achieved 71.9\% NDS and 67.7\% AMOTA on
the nuScenes test set. Code will be released at
\url{https://github.com/linxuewu/Sparse4D}.