Non-linear approximation of reflectance functions
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Multispectral Random Field Models for Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Synthesizing bidirectional texture functions for real-world surfaces
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Models of light reflection for computer synthesized pictures
SIGGRAPH '77 Proceedings of the 4th annual conference on Computer graphics and interactive techniques
Efficient rendering of spatial bi-directional reflectance distribution functions
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Wang Tiles for image and texture generation
ACM SIGGRAPH 2003 Papers
Efficient and realistic visualization of cloth
EGRW '03 Proceedings of the 14th Eurographics workshop on Rendering
Synthesis and Rendering of Bidirectional Texture Functions on Arbitrary Surfaces
IEEE Transactions on Visualization and Computer Graphics
TensorTextures: multilinear image-based rendering
ACM SIGGRAPH 2004 Papers
Non-linear Reflectance Model for Bidirectional Texture Function Synthesis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
BTF Image Space Utmost Compression and Modelling Method
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A psychophysically validated metric for bidirectional texture data reduction
ACM SIGGRAPH Asia 2008 papers
Probabilistic Discrete Mixtures Colour Texture Models
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Advanced textural representation of materials appearance
SIGGRAPH Asia 2011 Courses
A plausible texture enlargement and editing compound Markovian model
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Technical Section: Interactive high fidelity visualization of complex materials on the GPU
Computers and Graphics
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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 bump-mapping 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.