Journal
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
Volume -, Issue -, Pages 5372-5380Publisher
IEEE
DOI: 10.1109/ICCV.2017.573
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Funding
- NSF CAREER grant [CCF-1652569]
- NGIA grant [HM0476-17-1-2000]
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We propose the use of a light-weight setup consisting of a collocated camera and light source - commonly found on mobile devices - to reconstruct surface normals and spatially-varying BRDFs of near-planar material samples. A collocated setup provides only a 1-D univariate sampling of a 3-D isotropic BRDF. We show that a univariate sampling is sufficient to estimate parameters of commonly used analytical BRDF models. Subsequently, we use a dictionary-based reflectance prior to derive a robust technique for per-pixel normal and BRDF estimation. We demonstrate real-world shape and capture, and its application to material editing and classification, using real data acquired using a mobile phone.
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