Material Classification Using Raw Time-of-Flight Measurements

Handle URI:
http://hdl.handle.net/10754/623865
Title:
Material Classification Using Raw Time-of-Flight Measurements
Authors:
Su, Shuochen; Heide, Felix; Swanson, Robin Joseph ( 0000-0001-7528-0447 ) ; Klein, Jonathan; Callenberg, Clara; Hullin, Matthias; Heidrich, Wolfgang ( 0000-0002-4227-8508 )
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.
KAUST Department:
Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Citation:
Su S, Heide F, Swanson R, Klein J, Callenberg C, et al. (2016) Material Classification Using Raw Time-of-Flight Measurements. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/cvpr.2016.381.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Issue Date:
13-Dec-2016
DOI:
10.1109/cvpr.2016.381
Type:
Conference Paper
Sponsors:
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.
Additional Links:
http://ieeexplore.ieee.org/document/7780750/
Appears in Collections:
Conference Papers; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSu, Shuochenen
dc.contributor.authorHeide, Felixen
dc.contributor.authorSwanson, Robin Josephen
dc.contributor.authorKlein, Jonathanen
dc.contributor.authorCallenberg, Claraen
dc.contributor.authorHullin, Matthiasen
dc.contributor.authorHeidrich, Wolfgangen
dc.date.accessioned2017-05-31T11:23:10Z-
dc.date.available2017-05-31T11:23:10Z-
dc.date.issued2016-12-13en
dc.identifier.citationSu S, Heide F, Swanson R, Klein J, Callenberg C, et al. (2016) Material Classification Using Raw Time-of-Flight Measurements. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/cvpr.2016.381.en
dc.identifier.doi10.1109/cvpr.2016.381en
dc.identifier.urihttp://hdl.handle.net/10754/623865-
dc.description.abstractWe 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.en
dc.description.sponsorshipThis 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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7780750/en
dc.titleMaterial Classification Using Raw Time-of-Flight Measurementsen
dc.typeConference Paperen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journal2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)en
dc.contributor.institutionUniversity of British Columbiaen
dc.contributor.institutionStanford Universityen
dc.contributor.institutionUniversity of Bonnen
kaust.authorSu, Shuochenen
kaust.authorSwanson, Robin Josephen
kaust.authorHeidrich, Wolfgangen
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