Sparse algorithms offer great flexibility for multi-view temporal perception tasks. In this paper, we present an enhanced version of Sparse4D, in which we improve the temporal fusion module by implementing a recursive form of multi-frame feature sampling. By effectively decoupling image features and structured anchor features, Sparse4D enables a highly efficient transformation of temporal features, thereby facilitating temporal fusion solely through the frame-by-frame transmission of sparse features. The recurrent temporal fusion approach provides two main benefits. Firstly, it reduces the computational complexity of temporal fusion from $O(T)$ to $O(1)$, resulting in significant improvements in inference speed and memory usage. Secondly, it enables the fusion of long-term information, leading to more pronounced performance improvements due to temporal fusion. Our proposed approach, Sparse4Dv2, further enhances the performance of the sparse perception algorithm and achieves state-of-the-art results on the nuScenes 3D detection benchmark. Code will be available at \url{https://github.com/linxuewu/Sparse4D}.

翻译：稀疏算法为多视角时序感知任务提供了极大的灵活性。在本文中，我们提出了Sparse4D的增强版本，通过实现多帧特征采样的递归形式改进了时序融合模块。通过有效解耦图像特征与结构化锚点特征，Sparse4D实现了时序特征的高效转换，从而仅通过逐帧传递稀疏特征即可完成时序融合。这种循环时序融合方法具有两大优势：首先，它将时序融合的计算复杂度从$O(T)$降低至$O(1)$，显著提升了推理速度并减少了内存占用；其次，它支持长时序信息融合，使时序融合带来的性能提升更为显著。我们提出的Sparse4Dv2进一步增强了稀疏感知算法的性能，并在nuScenes 3D检测基准上取得了最先进的结果。代码将开源在\url{https://github.com/linxuewu/Sparse4D}。