Non-linear reflectance model for bidirectional texture function synthesis

Filip, Jiřı́; Haindl, Michal

doi:10.1109/icpr.2004.1334011

Cited by 5 publications

(10 citation statements)

References 12 publications

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“…5 . We have implemented several recently published BTF modeling approaches [8], [10], [12], [13], [26] and compared their basic features. All the intelligent sampling methods [13], [26] (BTF tiling), the reflectance model [8] (LMP), the GMRF model [10], the CAR 3D model [12] …”

Section: Resultsmentioning

confidence: 99%

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Extreme Compression and Modeling of Bidirectional Texture Function

Haindl

Filip

2007

IEEE Trans. Pattern Anal. Machine Intell.

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The recent advanced representation for realistic real-world materials in virtual reality applications is the Bidirectional Texture Function (BTF) which describes rough texture appearance for varying illumination and viewing conditions. Such a function can be represented by thousands of measurements (images) per material sample. The resulting BTF size excludes its direct rendering in graphical applications and some compression of these huge BTF data spaces is obviously inevitable. In this paper we present a novel, fast probabilistic model-based algorithm for realistic BTF modeling allowing an extreme compression with the possibility of a fast hardware implementation. Its ultimate aim is to create a visual impression of the same material without a pixel-wise correspondence to the original measurements.The analytical step of the algorithm starts with a BTF space segmentation and a range map estimation by photometric stereo of the BTF surface, followed by the spectral and spatial factorization of selected sub-space color texture images. Single mono-spectral band-limited factors are independently modeled by their dedicated spatial probabilistic model. During rendering, the sub-space images of arbitrary size are synthesized and both color (possibly multi-spectral) and range information is combined in a bumpmapping filter according to the view and illumination directions. The presented model offers a huge BTF compression ratio unattainable by any alternative sampling-based BTF synthesis method. Simultaneously this model can be used to reconstruct missing parts of the BTF measurement space.

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Section: Resultsmentioning

confidence: 99%

“…Here, the individual BTF pixels were represented by means of polynomials. Pixel-wise generalization of the Lafortune model computed for each view position separately was introduced in [8], [21]. These methods are very fast, however, they only enable moderate compression of the original BTF measurements.…”

Section: Btf Compression Methodsmentioning

confidence: 99%

Extreme Compression and Modeling of Bidirectional Texture Function

Haindl

Filip

2007

IEEE Trans. Pattern Anal. Machine Intell.

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“…This arrangement reduces the number of parameters to be stored, but simultaneously deteriorates approximation accuracy. In [23], only one lobe is used per color channel. The obtained results are then corrected by means of polynomials representing histogram matching functions between original and restored images.…”

Section: Btf Compression Based On Pixelwise Brdfmentioning

confidence: 99%

“…Unfortunately, reflectance values that are clearly and completely wrong result from the one-lobe LM model for certain combinations of illumination and viewing angles. The polynomial extension of one-lobe Lafortune model (3) (PLM RF) is proposed in [23], [24], which leads to the following formula:…”

Section: Polynomial Extension Of Lafortune Reflectancementioning

confidence: 99%

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Bidirectional Texture Function Modeling: A State of the Art Survey

Filip

Haindl

2009

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-An ever-growing number of real-world computer vision applications require classification, segmentation, retrieval, or realistic rendering of genuine materials. However, the appearance of real materials dramatically changes with illumination and viewing variations. Thus, the only reliable representation of material visual properties requires capturing of its reflectance in as wide range of light and camera position combinations as possible. This is a principle of the recent most advanced texture representation, the Bidirectional Texture Function (BTF). Multispectral BTF is a seven-dimensional function that depends on view and illumination directions as well as on planar texture coordinates. BTF is typically obtained by measurement of thousands of images covering many combinations of illumination and viewing angles. However, the large size of such measurements has prohibited their practical exploitation in any sensible application until recently. During the last few years, the first BTF measurement, compression, modeling, and rendering methods have emerged. In this paper, we categorize, critically survey, and psychophysically compare such approaches, which were published in this newly arising and important computer vision and graphics area.

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