A Data-Driven Reflectance Model

 

Wojciech Matusik Hanspeter Pfister Matt Brand Leonard McMillan
MIT MERL MERL MIT
 
Siggraph 2003

 

Renditions of materials generated using our model: steel teapot with greasy fingerprints (left), teapot with rust forming (right). Closeup pictures in the center. We used a spatially varying texture to interpolate between reflectance models for each point on the teapot.

 

Abstract

 

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 defne 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.

 

Citation

Wojciech Matusik, Hanspeter Pfister, Matt Brand, and Leonard McMillan. A Data-Driven Reflectance Model. ACM Transactions on Graphics. 22(3) 2002.

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