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A data-driven reflectance model

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TLDR
In this paper, a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data is presented, instead of using analytical reflectance models.
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...

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Proceedings ArticleDOI

Experimental analysis of BRDF models

TL;DR: This work evaluates several well-known analytical models in terms of their ability to fit measured BRDFs, and shows that using a sampled microfacet distribution computed from measurements improves the fit and qualitatively reproduces the measurements.
Proceedings ArticleDOI

Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination

TL;DR: A realtime shading model that uses independently estimated normal maps for the specular and diffuse color channels to reproduce some of the perceptually important effects of subsurface scattering is presented.
Journal ArticleDOI

Transient attributes for high-level understanding and editing of outdoor scenes

TL;DR: This work studies "transient scene attributes" -- high level properties which affect scene appearance, such as "snow", "autumn", "dusk", "fog", and defines 40 transient attributes and uses crowdsourcing to annotate thousands of images from 101 webcams to train regressors that can predict the presence of attributes in novel images.
Journal ArticleDOI

Shape and Spatially-Varying BRDFs from Photometric Stereo

TL;DR: A photometric stereo method designed for surfaces with spatially-varying BRDFs, including surfaces with both varying diffuse and specular properties, yielding accurate rerenderings under novel lighting conditions for a wide variety of objects.
Journal ArticleDOI

Example-based photometric stereo: shape reconstruction with general, varying BRDFs

TL;DR: This paper presents a technique for computing the geometry of objects with general reflectance properties from images that can handle objects with arbitrary and spatially-varying BRDFs.
References
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Journal ArticleDOI

Nonlinear dimensionality reduction by locally linear embedding.

TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI

A global geometric framework for nonlinear dimensionality reduction.

TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
Journal ArticleDOI

Reflectance and texture of real-world surfaces

TL;DR: A new texture representation called the BTF (bidirectional texture function) which captures the variation in texture with illumination and viewing direction is discussed, and a BTF database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions is presented.
Journal ArticleDOI

A Reflectance Model for Computer Graphics

TL;DR: In this article, the spectral energy distribution of the reflected light from an object made of a specific real material is obtained and a procedure for accurately reproducing the color associated with the spectrum is discussed.
Journal ArticleDOI

Average irregularity representation of a rough surface for ray reflection

TL;DR: In this paper, a new ray model is presented for the reflection of electromagnetic radiation from the rough air-material interface of a randomly rough surface, where the surface is modeled as an ellipsoid of revolution.
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