Reflectance field based real-time, high-quality rendering of bidirectional texture functions

Meseth, Jan; Müller, G.; Klein, Reinhard

doi:10.1016/j.cag.2003.10.011

Cited by 21 publications

(31 citation statements)

References 27 publications

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“…They can be applied for textured object retrieval if only a single illumination training image is available for each class. These properties are verified on Outex database (Ojala, Mäenpää, Pietikäinen, Viertola, Kyllönen & Huovinen, 2002), where texture images are illuminated under three different spectra, and University of Bonn BTF texture measurements (Meseth et al, 2003), where illumination sources are spanned over 75% of possible illumination direction above material samples. Preliminary experiments were published in Vacha & Haindl (2008), Vacha & Haindl (2007).…”

Section: Introductionmentioning

confidence: 81%

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Illumination Invariants Based on Markov Random Fields

Vácha¹,

Haindl²

2010

Pattern Recognition Recent Advances

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Section: Introductionmentioning

confidence: 81%

“…The experiment is performed on three different sets of BTF texture images. These BTF data are from the University of Bonn database (Meseth et al, 2003) and consists of fifteen BTF colour measurements Fig. 1.…”

Section: Methodsmentioning

confidence: 99%

Illumination Invariants Based on Markov Random Fields

Vácha¹,

Haindl²

2010

Pattern Recognition Recent Advances

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“…It should be noted that our technique is orthogonal to BTF compression techniques [38], [39], [40], since we focus on the compression of the global transfer matrix.…”

Section: Parameters and Performancementioning

confidence: 99%

Radiance Transfer Biclustering for Real-Time All-Frequency Biscale Rendering

Sun

Hou

Ren

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-We present a real-time algorithm to render all-frequency radiance transfer at both macroscale and mesoscale. At a mesoscale, the shading is computed on a per-pixel basis by integrating the product of the local incident radiance and a bidirectional texture function. While at a macroscale, the precomputed transfer matrix, which transfers the global incident radiance to the local incident radiance at each vertex, is losslessly compressed by a novel biclustering technique. The biclustering is directly applied on the radiance transfer represented in a pixel basis, on which the BTF is naturally defined. It exploits the coherence in the transfer matrix and a property of matrix element values to reduce both storage and runtime computation cost. Our new algorithm renders at real-time frame rates realistic materials and shadows under all-frequency direct environment lighting. Comparisons show that our algorithm is able to generate images that compare favorably with reference ray tracing results, and has obvious advantages over alternative methods in storage and preprocessing time.

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“…In [9] each pixel luminance was approximated by biquadratic polynomial, while chrominance was supposed to be constant. This approach (polynomial texture maps) suffers [11] with following specularity while grazing angles grow and with the diffuse material requirement. Several other solutions were published such as the per-pixel Lafortune model [2,10], or extension [2] by additional lookup table to handle occlusion effects and to correct colour hue values, a slight modification of a two lobes Lafortune model was implemented in the graphic card processing unit [11].…”

Section: Introductionmentioning

confidence: 99%

“…This approach (polynomial texture maps) suffers [11] with following specularity while grazing angles grow and with the diffuse material requirement. Several other solutions were published such as the per-pixel Lafortune model [2,10], or extension [2] by additional lookup table to handle occlusion effects and to correct colour hue values, a slight modification of a two lobes Lafortune model was implemented in the graphic card processing unit [11]. Alternative approaches to BTF synthesis are based either on intelligent sampling techniques [8], [3,4] or on adaptive multidimensional statistical models [6,5].…”

Section: Introductionmentioning

confidence: 99%