Sparse4D v3: Advancing End-to-End 3D Detection and Tracking
It addresses perception challenges in autonomous driving with incremental improvements to a query-based method.
This paper tackles 3D detection and tracking for autonomous driving by enhancing the Sparse4D framework with auxiliary training tasks and structural improvements, achieving gains of 3.0% mAP, 2.2% NDS, and 7.6% AMOTA on the nuScenes benchmark.
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}.