Matching Real Fabrics with Micro-Appearance Models

Micro-appearance models explicitly model the interaction of light with microgeometry at the fiber scale to produce realistic appearance. To effectively match them to real fabrics, we introduce a new appearance matching framework to determine their parameters. Given a micro-appearance model and photographs of the fabric under many different lighting conditions, we optimize for parameters that best match the photographs using a method based on calculating derivatives during rendering. This highly applicable framework, we believe, is a useful research tool because it simplifies development and testing of new models. Using the framework, we systematically compare several types of micro-appearance models. We acquired computed microtomography (micro CT) scans of several fabrics, photographed the fabrics under many viewing/illumination conditions, and matched several appearance models to this data. We compare a new fiber-based light scattering model to the previously used microflake model. We also compare representing cloth microgeometry using volumes derived directly from the micro CT data to using explicit fibers reconstructed from the volumes. From our comparisons, we make the following conclusions: (1) given a fiber-based scattering model, volume- and fiber-based microgeometry representations are capable of very similar quality, and (2) using a fiber-specific scattering model is crucial to good results as it achieves considerably higher accuracy than prior work.

[1]  Steve Marschner,et al.  Light scattering from human hair fibers , 2003, ACM Trans. Graph..

[2]  Steve Marschner,et al.  Capturing hair assemblies fiber by fiber , 2009, ACM Trans. Graph..

[3]  Steve Marschner,et al.  Specular reflection from woven cloth , 2012, TOGS.

[4]  James T. Kajiya,et al.  Rendering fur with three dimensional textures , 1989, SIGGRAPH.

[5]  Kun Zhou,et al.  Dynamic hair manipulation in images and videos , 2013, ACM Trans. Graph..

[6]  Stefan Hougardy,et al.  A simple approximation algorithm for the weighted matching problem , 2003, Inf. Process. Lett..

[7]  Steve Marschner,et al.  A Survey on Hair Modeling: Styling, Simulation, and Rendering , 2007, IEEE Transactions on Visualization and Computer Graphics.

[8]  Szymon Rusinkiewicz,et al.  Structure-aware hair capture , 2013, ACM Trans. Graph..

[9]  Reinhard Klein,et al.  Image-Based Reverse Engineering and Visual Prototyping of Woven Cloth , 2015, IEEE Transactions on Visualization and Computer Graphics.

[10]  Sylvain Paris,et al.  Capture of hair geometry from multiple images , 2004, ACM Trans. Graph..

[11]  Cheng Liang,et al.  IDSS: A Novel Representation for Woven Fabrics , 2013, IEEE Transactions on Visualization and Computer Graphics.

[12]  T. Shinohara,et al.  Extraction of Yarn Positional Information from a Three-dimensional CT Image of Textile Fabric using Yarn Tracing with a Filament Model for Structure Analysis , 2010 .

[13]  Jonathan T. Moon,et al.  A radiative transfer framework for rendering materials with anisotropic structure , 2010, ACM Trans. Graph..

[14]  Arno Zinke,et al.  Light Scattering from Filaments , 2007, IEEE Transactions on Visualization and Computer Graphics.

[15]  Shuang Zhao,et al.  Inverse volume rendering with material dictionaries , 2013, ACM Trans. Graph..

[16]  Erik L.J. Bohez,et al.  Computer Aided Modeling of Fiber Assemblies , 2006 .

[17]  Daniel Gembris,et al.  White matter fiber tractography via anisotropic diffusion simulation in the human brain , 2005, IEEE Transactions on Medical Imaging.

[18]  Henrik Wann Jensen,et al.  A practical microcylinder appearance model for cloth rendering , 2013, TOGS.

[19]  Steve Marschner,et al.  Building volumetric appearance models of fabric using micro CT imaging , 2011, ACM Trans. Graph..

[20]  Martin Hill,et al.  Eurographics Symposium on Rendering 2011 an Energy-conserving Hair Reflectance Model , 2022 .

[21]  Stephen Lin,et al.  Photorealistic rendering of knitwear using the lumislice , 2001, SIGGRAPH.

[22]  Reinhard Klein,et al.  A Volumetric Approach to Predictive Rendering of Fabrics , 2011, EGSR '11.

[23]  Nadia Magnenat-Thalmann,et al.  Visualization of Woven Cloth , 2003, Rendering Techniques.

[24]  Arno Zinke,et al.  Lighting hair from the inside , 2012, ACM Trans. Graph..

[25]  Ralf Sarlette,et al.  Efficient and Realistic Visualization of Cloth , 2003, Rendering Techniques.

[26]  Baining Guo,et al.  Modeling and rendering of realistic feathers , 2002, SIGGRAPH.

[27]  Gareth J. Barker,et al.  Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging , 2002, IEEE Transactions on Medical Imaging.

[28]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[29]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[30]  Ravi Ramamoorthi,et al.  Interactive albedo editing in path-traced volumetric materials , 2013, TOGS.

[31]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[32]  Anton Alstes Wang Tiles for Image and Texture Generation , 2004 .