Polynomial texture maps

Malzbender, Tom; Gelb, Dan; Wolters, Hans J.

doi:10.1145/383259.383320

Cited by 500 publications

(396 citation statements)

References 20 publications

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“…The Reflectance Transformation Imaging (RTI) approach [1] captures the broad range of reflectance characteristics for any surface. One implementation is PTM (Polynomial Texture Maps) [1], using a fixed standard digital camera at constant exposure and varying lighting.…”

Section: Introductionmentioning

confidence: 99%

“…One implementation is PTM (Polynomial Texture Maps) [1], using a fixed standard digital camera at constant exposure and varying lighting. A strength of PTM, in comparison to a simple Lambertian Photometric Stereo (PST) [2], is that the PTM applies a higher order polynomial regression from lighting direction parameters to model image values; thus it can better model real radiance, and to some degree apprehend intricate dependencies due to self-shadowing and interreflections.…”

Section: Introductionmentioning

confidence: 99%

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Robust estimation of surface properties and interpolation of shadow/specularity components

Drew

Hel-Or

Malzbender

et al. 2012

Image and Vision Computing

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The Polynomial Texture Map framework (PTM) extends the simple model of image formation from the Lambertian variant of Photometric Stereo (PST) to more general reflectances and to more complex-shaped surfaces. It forms an alternative method for apprehending object colour, albedo, and surface normals. Here we consider solving such a model in a robust version, not to date attempted for PTM, with the upshot that both shadows and specularities are identified automatically without the need for any thresholds. Instead of the linear model used in few-source PST for Lambertian surfaces, PTM adopts a higher degree polynomial model. PTM has two aims: interpolation of images for new lighting directions, and recovery of surface properties. Here we show that a robust approach is a good deal more accurate in recovering surface properties. For new-lighting interpolation, we demonstrate that a simple radial basis function interpolation can accurately interpolate specularities as well as attached and cast shadows even with a medium-sized image set, with no need for reflectance sharing across pixels or extremely large numbers of interpolation coefficients.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust estimation of surface properties and interpolation of shadow/specularity components

Drew

Hel-Or

Malzbender

et al. 2012

Image and Vision Computing

Self Cite

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“…Here one can see (Ramamoorthi and Hanrahan, 2001b;Driscoll and Healy, 1994) (Malzbender et al, 2001). 2.…”

Section: Appendixmentioning

confidence: 91%

Appearance Sampling of Real Objects for Variable Illumination

2007

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Abstract. The appearance of an object greatly changes under different lighting conditions. Even so, previous studies have demonstrated that the appearance of an object under varying illumination conditions can be represented by a linear subspace. A set of basis images spanning such a linear subspace can be obtained by applying the principal component analysis (PCA) for a large number of images taken under different lighting conditions. Since little is known about how to sample the appearance of an object in order to correctly obtain its basis images, it was a common practice to use as many input images as possible. In this study, we present a novel method for analytically obtaining a set of basis images of an object for varying illumination from input images of the object taken properly under a set of light sources, such as point light sources or extended light sources. Our proposed method incorporates the sampling theorem of spherical harmonics for determining a set of lighting directions to efficiently sample the appearance of an object. We further consider the issue of aliasing caused by insufficient sampling of the object's appearance. In particular, we investigate the effectiveness of using extended light sources for modeling the appearance of an object under varying illumination without suffering the aliasing caused by insufficient sampling of its appearance.

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“…The majority of these methods requires a specialized hardware setup [6][7][8][9][10], have long acquisition/processing times [11,12] or are not able to scan specular surfaces [2,3]. In our work we will use a slightly adapted version of the Gray code based approach of Francken et al [1], employing a screen-camera setup as acquistion setup.…”

Section: Fine-scale Geometrymentioning

confidence: 99%

“…As storing individual data samples of densely sampled BRDFs is memory inefficient, often approximating models are fitted through the large data collection. This is either achieved by fitting an analytical model [18][19][20][21][22], or projecting the data to polynomial [23,6], spherical harmonics [24][25][26] or wavelet bases [27,28]. For the sake of simplicity as well as compatibility with known tools, in our work we will employ a simple analytical Phong model [29] where the glossiness is represented by a single exponent parameter.…”

Section: Reflectancementioning

confidence: 99%

Gloss and Normal Map Acquisition of Mesostructures Using Gray Codes

Francken

Cuypers

Mertens

et al. 2009

Advances in Visual Computing

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Abstract. We propose a technique for gloss and normal map acquisition of finescale specular surface details, or mesostructure. Our main goal is to provide an efficient, easily applicable, but sufficiently accurate method to acquire mesostructures. We therefore employ a setup consisting of inexpensive and accessible components, including a regular computer screen and a digital still camera. We extend the Gray code based normal map acquisition approach of Francken et al. [1] which utilizes a similar setup. The quality of the original method is retained and without requiring any extra input data we are able to extract per pixel glossiness information. In the paper we show the theoretical background of the method as well as results on real-world specular mesostructures.

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Polynomial texture maps

Cited by 500 publications

References 20 publications

Robust estimation of surface properties and interpolation of shadow/specularity components

Robust estimation of surface properties and interpolation of shadow/specularity components

Appearance Sampling of Real Objects for Variable Illumination

Gloss and Normal Map Acquisition of Mesostructures Using Gray Codes

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