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CMT: Context-Matching-Guided Transformer for 3D Tracking in Point Clouds

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

How to effectively match the target template features with the search area is the core problem in point-cloud-based 3D single object tracking. However, in the literature, most of the methods focus on devising sophisticated matching modules at point-level, while overlooking the rich spatial context information of points. To this end, we propose Context-Matching-Guided Transformer (CMT), a Siamese tracking paradigm for 3D single object tracking. In this work, we first leverage the local distribution of points to construct a horizontally rotation-invariant contextual descriptor for both the template and the search area. Then, a novel matching strategy based on shifted windows is designed for such descriptors to effectively measure the template-search contextual similarity. Furthermore, we introduce a target-specific transformer and a spatial-aware orientation encoder to exploit the target-aware information in the most contextually relevant template points, thereby enhancing the search feature for a better target proposal. We conduct extensive experiments to verify the merits of our proposed CMT and report a series of new state-of-the-art records on three widely-adopted datasets.

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Acknowledgements.

This work was supported by the National Natural Science Foundation of China under Contract U20A20183, 61836011 and 62021001. It was also supported by the GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC.

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Correspondence to Wengang Zhou or Houqiang Li .

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Guo, Z., Mao, Y., Zhou, W., Wang, M., Li, H. (2022). CMT: Context-Matching-Guided Transformer for 3D Tracking in Point Clouds. 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 13682. Springer, Cham. https://doi.org/10.1007/978-3-031-20047-2_6

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