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- W4378468525 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}." @default.
- W4378468525 created "2023-05-27" @default.
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- W4378468525 date "2023-05-23" @default.
- W4378468525 modified "2023-09-26" @default.
- W4378468525 title "Sparse4D v2: Recurrent Temporal Fusion with Sparse Model" @default.
- W4378468525 doi "https://doi.org/10.48550/arxiv.2305.14018" @default.
- W4378468525 hasPublicationYear "2023" @default.
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