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}.

翻译：在自动驾驶感知系统中，三维检测与跟踪是两项基础任务。本文在Sparse4D框架基础上对该领域进行了更深入的研究。我们引入了两项辅助训练任务（时序实例去噪和质量估计），并提出了解耦注意力机制以改进网络结构，从而显著提升了检测性能。此外，我们通过一种简洁的方式将检测器扩展为跟踪器——在推理过程中分配实例ID，进一步凸显了基于查询的算法的优势。在nuScenes基准上的大量实验验证了所提改进的有效性。以ResNet50为主干网络时，我们在mAP、NDS和AMOTA指标上分别提升了3.0%、2.2%和7.6%，最终达到46.9%、56.1%和49.0%。我们的最佳模型在nuScenes测试集上取得了71.9%的NDS和67.7%的AMOTA。代码将于\url{https://github.com/linxuewu/Sparse4D}发布。