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dc.contributor.authorWerner, Sebastianen_US
dc.contributor.authorIseringhausen, Julianen_US
dc.contributor.authorCallenberg, Claraen_US
dc.contributor.authorHullin, Matthiasen_US
dc.contributor.editorSchulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michaelen_US
dc.date.accessioned2019-09-29T06:45:49Z
dc.date.available2019-09-29T06:45:49Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-098-7
dc.identifier.urihttps://doi.org/10.2312/vmv.20191315
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20191315
dc.description.abstractStructured-light methods remain one of the leading technologies in high quality 3D scanning, specifically for the acquisition of single objects and simple scenes. For more complex scene geometries, however, non-local light transport (e.g. interreflections, sub-surface scattering) comes into play, which leads to errors in the depth estimation. Probing the light transport tensor, which describes the global mapping between illumination and observed intensity under the influence of the scene can help to understand and correct these errors, but requires extensive scanning. We aim to recover a 3D subset of the full 4D light transport tensor, which represents the scene as illuminated by line patterns, rendering the approach especially useful for triangulation methods. To this end we propose a frequency-domain approach based on spectral estimation to reduce the number of required input images. Our method can be applied independently on each pixel of the observing camera, making it perfectly parallelizable with respect to the camera pixels. The result is a closed-form representation of the scene reflection recorded under line illumination, which, if necessary, masks pixels with complex global light transport contributions and, if possible, enables the correction of such measurements via data-driven semi-automatic editing.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectShape inference
dc.subjectReconstruction
dc.titleTrigonometric Moments for Editable Structured Light Range Findingen_US
dc.description.seriesinformationVision, Modeling and Visualization
dc.description.sectionheadersImaging
dc.identifier.doi10.2312/vmv.20191315
dc.identifier.pages27-35


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This item appears in the following Collection(s)

  • VMV19
    ISBN 978-3-03868-098-7

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