In this paper, we propose a long-sequence modeling framework, named StreamPETR, for multi-view 3D object detection. Built upon the sparse query design in the PETR series, we systematically develop an object-centric temporal mechanism. The model is performed in an online manner and the long-term historical information is propagated through object queries frame by frame. Besides, we introduce a motion-aware layer normalization to model the movement of the objects. StreamPETR achieves significant performance improvements only with negligible computation cost, compared to the single-frame baseline. On the standard nuScenes benchmark, it is the first online multi-view method that achieves comparable performance (67.6% NDS & 65.3% AMOTA) with lidar-based methods. The lightweight version realizes 45.0% mAP and 31.7 FPS, outperforming the state-of-the-art method (SOLOFusion) by 2.3% mAP and 1.8x faster FPS. Code has been available at https://github.com/exiawsh/StreamPETR.git.

翻译：本文提出了一种名为StreamPETR的长序列建模框架，用于多视角三维目标检测。该框架基于PETR系列的稀疏查询设计，系统性地开发了面向物体的时序机制。模型以在线方式运行，通过逐帧传递物体查询来传播长期历史信息。此外，我们引入运动感知层归一化以建模物体的运动。与单帧基线相比，StreamPETR仅以可忽略的计算代价实现了显著的性能提升。在标准nuScenes基准测试中，它是首个性能（NDS 67.6%与AMOTA 65.3%）与基于激光雷达的方法相当的多视角在线检测方法。其轻量版本实现了45.0%的mAP和31.7 FPS，相比当前最优方法（SOLOFusion）mAP提升了2.3%，帧率提升了1.8倍。代码已开源：https://github.com/exiawsh/StreamPETR.git。