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Sparse4D v2: Recurrent Temporal Fusion with Sparse Model

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arxiv 2305.14018 v2 pith:ZP43MHL4 submitted 2023-05-23 cs.CV

Xuewu Lin , Tianwei Lin , Zixiang Pei , Lichao Huang , Zhizhong Su This is my paper · ORCID
classification cs.CV
keywords temporalfusionfeaturessparsesparse4dapproachenablesimprovements
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abstract

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}.

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Forward citations

Cited by 4 Pith papers

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    Kerr-Schild double copy of the RS II black string produces a sourceless Maxwell single copy and a warp-induced massive scalar zeroth copy, with an alternative splitting giving inequivalent gauge and scalar fields.

  2. MambaMap: Online Vectorized HD Map Construction using State Space Model

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MambaMap fuses four previous frames of BEV features and instance queries via gated state space layers, beating prior HD map construction methods on nuScenes and Argoverse2.

  3. MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A camera-LiDAR 3D detector built around a hybrid local-global Mamba block with height-fidelity LiDAR encoding reports 75.0 NDS on nuScenes validation, outperforming prior transformer-based fusion methods.

  4. DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos

    cs.CV 2025-06 conditional novelty 5.0 of 10

    DySS combines state-space feature learning with dynamic query merging and pruning to improve both accuracy and speed for camera-based 3D detection on nuScenes.

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