Appearance based object modeling using texture database: acquisition, compression and rendering
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Image-Based Reconstruction of Spatially Varying Materials
Proceedings of the 12th Eurographics Workshop on Rendering Techniques
A data-driven reflectance model
ACM SIGGRAPH 2003 Papers
TensorTextures: multilinear image-based rendering
ACM SIGGRAPH 2004 Papers
Inverse shade trees for non-parametric material representation and editing
ACM SIGGRAPH 2006 Papers
AppProp: all-pairs appearance-space edit propagation
ACM SIGGRAPH 2008 papers
Modeling anisotropic surface reflectance with example-based microfacet synthesis
ACM SIGGRAPH 2008 papers
Multivariate Regression and Machine Learning with Sums of Separable Functions
SIAM Journal on Scientific Computing
Tensor Decompositions and Applications
SIAM Review
Manifold bootstrapping for SVBRDF capture
ACM SIGGRAPH 2010 papers
A coaxial optical scanner for synchronous acquisition of 3D geometry and surface reflectance
ACM SIGGRAPH 2010 papers
Classification with sums of separable functions
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Experimental analysis of BRDF models
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
Reflectance sharing: image-based rendering from a sparse set of images
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
Efficient basis decomposition for scattered reflectance data
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
Rapid synchronous acquisition of geometry and appearance of cultural heritage artefacts
VAST'05 Proceedings of the 6th International conference on Virtual Reality, Archaeology and Intelligent Cultural Heritage
Integrated high-quality acquisition of geometry and appearance for cultural heritage
VAST'11 Proceedings of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage
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In recent years, measuring surface reflectance has become an established method for high quality renderings. In this context, especially non-parametric representations got a lot of attention as they allow for a very accurate representation of complex reflectance behavior. However, the acquisition of this data is a challenging task especially if complex object geometry is involved. Capturing images of the object under varying illumination and view conditions results in irregular angular samplings of the reflectance function with a limited angular resolution. Classical data-driven techniques, like tensor factorization, are not well suited for such data sets as they require a resampling of the high dimensional measurement data to a regular grid. This grid has to be on a much higher angular resolution to avoid resampling artifacts which in turn would lead to data sets of enormous size. To overcome these problems we introduce a novel, compact data-driven representation of reflectance functions based on a sum of separable functions which are fitted directly to the irregular set of data without any further resampling. The representation allows for efficient rendering and is also well suited for GPU applications. By exploiting spatial coherence of the reflectance function over the object a very precise reconstruction even of specular materials becomes possible already with a sparse input sampling. This would be impossible using standard data interpolation techniques. Since our algorithm exclusively operates on the compressed representation, it is both efficient in terms of memory use and computational complexity, depending only sub-linearly on the size of the fully tabulated data. The quality of the reflectance function is evaluated on synthetic data sets as ground truth as well as on real world measurements. © 2012 Wiley Periodicals, Inc.