A data-driven reflectance model

By Wojciech Matusik. ACM Transactions on Graphics, 2003

1,010 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/50ba65f0dc271c4af6a8423dc6a9c4d91eaf37da

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Citations in the corpus

Displacement interpolation using Lagrangian mass transport (2011, methodology): “That is, our interpolated BRDFs never reﬂect more light than they receive as long as the source and target BRDFs have the same property.”
Transient attributes for high-level understanding and editing of outdoor scenes (2014, background): “High level attributes have even been deﬁned for BRDF appearance models to support navigation and manipulation within the space of valid BRDFs [Matusik et al. 2003].”
OpenSurfaces (2013, methodology): “Our annotation pipeline takes as input a set of consumer photos depicting one or more surfaces, in context, in an interior scene, such as a kitchen.”
Example-based photometric stereo: shape reconstruction with general, varying BRDFs (2005, result): “Studies based on empirical BRDF measurements may help determine what objects would comprise such a set, in a similar vein to the work of Matusik et al. [20], [21].”
Shape and Spatially-Varying BRDFs from Photometric Stereo (2010, methodology): “Fig. 6. Examples of the synthetic images used for reconstruction, with associated material maps used to blend the two materials “dark-red-paint” and “blue-acrylic” from the MERL BRDF database [ 18 ].”
Learning to reconstruct shape and spatially-varying reflectance from a single image (2018, neutral): “, photometric stereo methods that reconstruct shape assuming known reflectance and lighting [Woodham 1980], and BRDF acquisition methods that reconstruct material reflectance assuming known shape and lighting [Marschner et al. 1999; Matusik et al. 2003].”