Material Classification Using Raw Time-of-Flight Measurements

Shuochen Su; Felix Heide; Robin J. Swanson; Jonathan Klein; Clara Callenberg; Matthias Hullin; Wolfgang Heidrich

doi:10.1109/cvpr.2016.381

Material Classification Using Raw Time-of-Flight Measurements

Shuochen Su, Felix Heide, Robin J. Swanson, Jonathan Klein, Clara Callenberg, Matthias Hullin, Wolfgang Heidrich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

46 Scopus citations

Abstract

We propose a material classification method using raw time-of-flight (ToF) measurements. ToF cameras capture the correlation between a reference signal and the temporal response of material to incident illumination. Such measurements encode unique signatures of the material, i.e. the degree of subsurface scattering inside a volume. Subsequently, it offers an orthogonal domain of feature representation compared to conventional spatial and angular reflectance-based approaches. We demonstrate the effectiveness, robustness, and efficiency of our method through experiments and comparisons of real-world materials.

Original language	English (US)
Title of host publication	2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)	9781467388511
DOIs	https://doi.org/10.1109/cvpr.2016.381
State	Published - Dec 13 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported through the X-Rite Chair and Graduate School for Digital Material Appearance, the German Research Foundation, Grant HU 2273/2-1, the Baseline Funding of the King Abdullah University of Science and Technology, and a UBC 4 Year Fellowship.

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10.1109/cvpr.2016.381

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@inproceedings{8f61897e2e294235818f1ae41c7f5bf3,

title = "Material Classification Using Raw Time-of-Flight Measurements",

abstract = "We propose a material classification method using raw time-of-flight (ToF) measurements. ToF cameras capture the correlation between a reference signal and the temporal response of material to incident illumination. Such measurements encode unique signatures of the material, i.e. the degree of subsurface scattering inside a volume. Subsequently, it offers an orthogonal domain of feature representation compared to conventional spatial and angular reflectance-based approaches. We demonstrate the effectiveness, robustness, and efficiency of our method through experiments and comparisons of real-world materials.",

author = "Shuochen Su and Felix Heide and Swanson, {Robin J.} and Jonathan Klein and Clara Callenberg and Matthias Hullin and Wolfgang Heidrich",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was supported through the X-Rite Chair and Graduate School for Digital Material Appearance, the German Research Foundation, Grant HU 2273/2-1, the Baseline Funding of the King Abdullah University of Science and Technology, and a UBC 4 Year Fellowship.",

year = "2016",

month = dec,

day = "13",

doi = "10.1109/cvpr.2016.381",

language = "English (US)",

isbn = "9781467388511",

booktitle = "2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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TY - GEN

T1 - Material Classification Using Raw Time-of-Flight Measurements

AU - Su, Shuochen

AU - Heide, Felix

AU - Swanson, Robin J.

AU - Klein, Jonathan

AU - Callenberg, Clara

AU - Hullin, Matthias

AU - Heidrich, Wolfgang

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was supported through the X-Rite Chair and Graduate School for Digital Material Appearance, the German Research Foundation, Grant HU 2273/2-1, the Baseline Funding of the King Abdullah University of Science and Technology, and a UBC 4 Year Fellowship.

PY - 2016/12/13

Y1 - 2016/12/13

N2 - We propose a material classification method using raw time-of-flight (ToF) measurements. ToF cameras capture the correlation between a reference signal and the temporal response of material to incident illumination. Such measurements encode unique signatures of the material, i.e. the degree of subsurface scattering inside a volume. Subsequently, it offers an orthogonal domain of feature representation compared to conventional spatial and angular reflectance-based approaches. We demonstrate the effectiveness, robustness, and efficiency of our method through experiments and comparisons of real-world materials.

AB - We propose a material classification method using raw time-of-flight (ToF) measurements. ToF cameras capture the correlation between a reference signal and the temporal response of material to incident illumination. Such measurements encode unique signatures of the material, i.e. the degree of subsurface scattering inside a volume. Subsequently, it offers an orthogonal domain of feature representation compared to conventional spatial and angular reflectance-based approaches. We demonstrate the effectiveness, robustness, and efficiency of our method through experiments and comparisons of real-world materials.

UR - http://hdl.handle.net/10754/623865

UR - http://ieeexplore.ieee.org/document/7780750/

U2 - 10.1109/cvpr.2016.381

DO - 10.1109/cvpr.2016.381

M3 - Conference contribution

SN - 9781467388511

BT - 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Material Classification Using Raw Time-of-Flight Measurements

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