期刊
ACM TRANSACTIONS ON GRAPHICS
卷 22, 期 3, 页码 759-769出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/882262.882343
关键词
light reflection models; photometric measurements; reflectance; BRDF; image-based modeling
We present a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data. Instead of using analytical reflectance models, we represent each BRDF as a dense set of measurements. This allows us to interpolate and extrapolate in the space of acquired BRDFs to create new BRDFs. We treat each acquired BRDF as a single high-dimensional vector taken from a space of all possible BRDFs. We apply both linear (subspace) and non-linear (manifold) dimensionality reduction tools in an effort to discover a lower-dimensional representation that characterizes our measurements. We let users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space. On the low-dimensional manifold, movement along these directions produces novel but valid BRDFs.
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