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Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13697))

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Abstract

3D point-clouds and 2D images are different visual representations of the physical world. While human vision can understand both representations, computer vision models designed for 2D image and 3D point-cloud understanding are quite different. Our paper explores the potential of transferring 2D model architectures and weights to understand 3D point-clouds, by empirically investigating the feasibility of the transfer, the benefits of the transfer, and shedding light on why the transfer works. We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds. Specifically, we transfer the image-pretrained model to a point-cloud model by copying or inflating the weights. We find that finetuning the transformed image-pretrained models (FIP) with minimal efforts—only on input, output, and normalization layers—can achieve competitive performance on 3D point-cloud classification, beating a wide range of point-cloud models that adopt task-specific architectures and use a variety of tricks. When finetuning the whole model, the performance gets further improved. Meanwhile, FIP improves data efficiency, reaching up to 10.0 top-1 accuracy percent on few-shot classification. It also speeds up the training of point-cloud models by up to 11.1x for a target accuracy (e.g., 90% accuracy). Lastly, we provide an explanation of the image to point-cloud transfer from the aspect of neural collapse. The code is available at: https://github.com/chenfengxu714/image2point.

C. Xu and S. Yang—Equal contribution.

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Acknowledgements

Co-authors from UC Berkeley were sponsored by Berkeley Deep Drive (BDD). Tomer Galanti’s contribution was supported by the Center for Minds, Brains and Machines (CBMM), funded by NSF STC award CCF-1231216.

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Authors and Affiliations

  1. University of California, Berkeley, Berkeley, USA

    Chenfeng Xu, Shijia Yang, Xiangyu Yue, Bohan Zhai, Wei Zhan, Kurt Keutzer & Masayoshi Tomizuka

  2. Massachusetts Institute of Technology, Cambridge, USA

    Tomer Galanti

  3. Meta Reality Labs, Menlo Park, USA

    Bichen Wu & Peter Vajda

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  1. Chenfeng Xu
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Correspondence to Bichen Wu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Xu, C. et al. (2022). Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models. 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 13697. Springer, Cham. https://doi.org/10.1007/978-3-031-19836-6_36

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