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Sparse4D v3: Advancing End-to-End 3D Detection and Tracking

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arxiv 2311.11722 v1 pith:MUCG43MC submitted 2023-11-20 cs.CV cs.AIcs.RO

Xuewu Lin , Zixiang Pei , Tianwei Lin , Lichao Huang , Zhizhong Su This is my paper
classification cs.CVcs.AIcs.RO
keywords detectionsparse4damotaenhancementsimprovementsinstancenuscenestasks
verification ladder T0 review T1 audit T2 compute T3 formal
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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}.

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Cited by 5 Pith papers

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