  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.
