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MPPNet: Multi-frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performance 3D detection framework, named MPPNet, for 3D temporal object detection with point cloud sequences. We propose a novel three-hierarchy framework with proxy points for multi-frame feature encoding and interactions to achieve better detection. The three hierarchies conduct per-frame feature encoding, short-clip feature fusion, and whole-sequence feature aggregation, respectively. To enable processing long-sequence point clouds with reasonable computational resources, intra-group feature mixing and inter-group feature attention are proposed to form the second and third feature encoding hierarchies, which are recurrently applied for aggregating multi-frame trajectory features. The proxy points not only act as consistent object representations for each frame, but also serve as the courier to facilitate feature interaction between frames. The experiments on large Waymo Open dataset show that our approach outperforms state-of-the-art methods with large margins when applied to both short (e.g., 4-frame) and long (e.g., 16-frame) point cloud sequences. Code will be publicly available at https://github.com/open-mmlab/OpenPCDet.

X. Chen and S. Shi—Equal contribution

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Acknowledgement

This work is supported in part by NVIDIA, in part by Centre for Perceptual and Interactive Intelligence Limited, in part by the General Research Fund through the Research Grants Council of Hong Kong under Grants (Nos. 14204021, 14207319), in part by CUHK Strategic Fund. Hongsheng Li is also a PI at Centre for Perceptual and Interactive Intelligence Limited.

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Correspondence to Shaoshuai Shi or Hongsheng Li .

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Chen, X., Shi, S., Zhu, B., Cheung, K.C., Xu, H., Li, H. (2022). MPPNet: Multi-frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_39

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