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Streamable Neural Fields

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13680))

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Abstract

Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations. Since they preserve signals in their network parameters, the data transfer by sending and receiving the entire model parameters prevents this emerging technology from being used in many practical scenarios. We propose streamable neural fields, a single model that consists of executable sub-networks of various widths. The proposed architectural and training techniques enable a single network to be streamable over time and reconstruct different qualities and parts of signals. For example, a smaller sub-network produces smooth and low-frequency signals, while a larger sub-network can represent fine details. Experimental results have shown the effectiveness of our method in various domains, such as 2D images, videos, and 3D signed distance functions. Finally, we demonstrate that our proposed method improves training stability, by exploiting parameter sharing. Our code is available at https://github.com/jwcho5576/streamable_nf.

J. Cho and S. Nam—Equal contribution, alphabetically ordered.

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Acknowledgements

This research was supported by the Ministry of Science and ICT (MSIT) of Korea, under the National Research Foundation (NRF) grant (2021R1F1A1061259), Institute of Information and Communication Technology Planning Evaluation (IITP) grants for the AI Graduate School program (IITP-2019-0-00421) and Artificial Intelligence Innovation Hub program (2021-0-02068).

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Authors and Affiliations

  1. Department of Artificial Intelligence, Sungkyunkwan University, Suwon, South Korea

    Junwoo Cho, Seungtae Nam, Daniel Rho, Jong Hwan Ko & Eunbyung Park

  2. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea

    Jong Hwan Ko & Eunbyung Park

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  1. Junwoo Cho
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  2. Seungtae Nam
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  5. Eunbyung Park
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Correspondence to Eunbyung Park .

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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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Cho, J., Nam, S., Rho, D., Ko, J.H., Park, E. (2022). Streamable Neural Fields. 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 13680. Springer, Cham. https://doi.org/10.1007/978-3-031-20044-1_34

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  • DOI: https://doi.org/10.1007/978-3-031-20044-1_34

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