Compressive Higher-order Sparse and Low-Rank Acquisition with a Hyperspectral Light Stage

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Zitierfähiger Link (URI): http://nbn-resolving.de/urn:nbn:de:bsz:21-opus-62077
http://hdl.handle.net/10900/44129
Dokumentart: Verschiedenartige Texte
Erscheinungsdatum: 2012
Originalveröffentlichung: WSI ; 2012 ; 01
Sprache: Englisch
Fakultät: 9 Sonstige / Externe
7 Mathematisch-Naturwissenschaftliche Fakultät
Fachbereich: Sonstige/Externe
Informatik
DDC-Klassifikation: 004 - Informatik
Schlagworte: Computergraphik
Freie Schlagwörter:
Reflectance , Sampling , Scanning , Multispectral , Hyperspectral , BRDF , BTF , Lightstage
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Abstract:

Compressive sparse and low-rank recovery (CSLR) is a novel method for compressed sensing deriving a low-rank and a sparse data terms from randomized projection measurements. While previous approaches either applied compressive measurements to phenomena assumed to be sparse or explicitly assume and measure low-rank approximations, CSLR is inherently robust if any such assumption might be violated. In this paper, we will derive CSLR using Fixed-Point Continuation algorithms, and extend this approach in order to exploit the correlation in high-order dimensions to further reduce the number of captured samples. Though generally applicable, we demonstrate the effectiveness of our approach on data sets captured with a novel hyperspectral light stage that can emit a distinct spectrum from each of the 196 light source directions enabling bispectral measurements of reflectance from arbitrary viewpoints. Bispectral reflectance fields and BTFs are faithfully reconstructed from a small number of compressed measurements.

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