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McGrids: Monte Carlo-Driven Adaptive Grids for Iso-Surface Extraction

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Iso-surface extraction from an implicit field is a fundamental process in various applications of computer vision and graphics. When dealing with geometric shapes with complicated geometric details, many existing algorithms suffer from high computational costs and memory usage. This paper proposes McGrids, a novel approach to improve the efficiency of iso-surface extraction. The key idea is to construct adaptive grids for iso-surface extraction rather than using a simple uniform grid as prior art does. Specifically, we formulate the problem of constructing adaptive grids as a probability sampling problem, which is then solved by Monte Carlo process. We demonstrate McGrids’ capability with extensive experiments from both analytical SDFs computed from surface meshes and learned implicit fields from real multiview images. The experiment results show that our McGrids can significantly reduce the number of implicit field queries, resulting in significant memory reduction, while producing high-quality meshes with rich geometric details.

D. Ren and H. Shi—These authors contributed equally to this work.

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Acknowledgements

This work is supported by MOE AcRF Tier 1 Grant of Singapore (RG12/22), and also by the RIE2025 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) (Award I2301E0026), administered by A*STAR, as well as supported by Alibaba Group and NTU Singapore. Daxuan Ren was also partially supported by Autodesk Singapore.

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Authors and Affiliations

  1. College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore

    Daxuan Ren, Hezi Shi, Jianmin Zheng & Jianfei Cai

  2. Department of Data Science and AI, Monash University, Clayton, Australia

    Jianfei Cai

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  1. Daxuan Ren
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  2. Hezi Shi
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  3. Jianmin Zheng
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  4. Jianfei Cai
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Corresponding author

Correspondence to Daxuan Ren .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Ren, D., Shi, H., Zheng, J., Cai, J. (2025). McGrids: Monte Carlo-Driven Adaptive Grids for Iso-Surface Extraction. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15115. Springer, Cham. https://doi.org/10.1007/978-3-031-72998-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-72998-0_8

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