Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
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Cited by 66 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 313 papers in the map (data through 7 Oct 2026). Builds on 56 of its 66 references that are in the map. First posted on arXiv 21 Mar 2023.
Builds on
56 of 66 references.
References · 56
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