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Spectral Subsurface Scattering for Material Classification

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

This study advances material classification using Spectral Sub-Surface Scattering (\(\mathcal {S}^{4}\)) measurements. While spectrum and subsurface scattering measurements have individually been used in material classification, we argue that the strong spectral dependence of subsurface scattering lends itself to highly discriminative features. However, obtaining \(\mathcal {S}^{4}\) measurements requires a time-consuming hyperspectral scan. We avoid this by showing that a carefully chosen 2D projection of the \(\mathcal {S}^{4}\) point spread function is sufficient for material estimation. We also design and implement a novel imaging setup, consisting of a point illumination and a spectrally-dispersing camera, to make the desired 2D projections. Finally, through comprehensive experiments, we demonstrate the superiority of \(\mathcal {S}^{4}\) imaging over spectral and sub-surface scattering measurements for the task of material classification.

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Acknowledgements

This work was supported in part by the National Geospatial-Intelligence Agency’s Academic Research Program (Grant# HM0476-22-1-0004). The views expressed in this paper do not necessarily reflect those of the National Geospatial-Intelligence Agency, the Department of Defense, or any other department or agency of the US Government. The authors were also supported in part by a Sony Faculty Innovation Award, a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, and a TCS Presidential Fellowship. The authors thank collaborators at Sony Corp., specifically, Ryuichi Tadano, Tuo Zhuang, Koya Kobayashi, Ilya Reshetouski, Hideki Oyaizu, and Jun Murayama, for valuable discussions leading to this work.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Haejoon Lee & Aswin C. Sankaranarayanan

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  1. Haejoon Lee
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Correspondence to Haejoon Lee .

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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, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, 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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Lee, H., Sankaranarayanan, A.C. (2025). Spectral Subsurface Scattering for Material Classification. 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 15063. Springer, Cham. https://doi.org/10.1007/978-3-031-72652-1_7

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  • DOI: https://doi.org/10.1007/978-3-031-72652-1_7

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