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Resolution-Free Point Cloud Sampling Network with Data Distillation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13662))

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Abstract

Down-sampling algorithms are adopted to simplify the point clouds and save the computation cost on subsequent tasks. Existing learning-based sampling methods often need to train a big sampling network to support sampling under different resolutions, which must generate sampled points with the costly maximum resolution even if only low-resolution points need to be sampled. In this work, we propose a novel resolution-free point clouds sampling network to directly sample the original point cloud to different resolutions, which is conducted by optimizing non-learning-based initial sampled points to better positions. Besides, we introduce data distillation to assist the training process by considering the differences between task network outputs from original point clouds and sampled points. Experiments on point cloud reconstruction and recognition tasks demonstrate that our method can achieve SOTA performances with lower time and memory cost than existing learning-based sampling strategies. Codes are available at https://github.com/Tianxinhuang/PCDNet.

T. Huang and J. Zhang—Indicates equal contributions.

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Acknowledgement

We thank all authors, reviewers and the chair for the excellent contributions. This work is supported by the National Science Foundation 62088101.

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Correspondence to Yong Liu .

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Huang, T., Zhang, J., Chen, J., Liu, Y., Liu, Y. (2022). Resolution-Free Point Cloud Sampling Network with Data Distillation. 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 13662. Springer, Cham. https://doi.org/10.1007/978-3-031-20086-1_4

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  • DOI: https://doi.org/10.1007/978-3-031-20086-1_4

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  • Online ISBN: 978-3-031-20086-1

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