“Matching Real Fabrics With Micro-Appearance Models” by Khungurn, Schroeder, Zhao, Bala and Marshier

  • ©Pramook Khungurn, Daniel Schroeder, Shuang Zhao, Kavita Bala, and Steve Marshier

Conference:


Type:


Title:

    Matching Real Fabrics With Micro-Appearance Models

Session/Category Title: CLOTH

Presenter(s)/Author(s):


Moderator(s):


Abstract:


    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.

References:


    1. N. Adabala, N. Magnenat-Thalmann, and G. Fei. 2003. Visualization of woven cloth. In Proceedings of the 14th Eurographics Workshop on Rendering (EGRW’03). Eurographics Association, Aire-la-Ville, Switzerland, 178–185. 
    2. P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi. 2000. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med 44, 625–632.
    3. L. Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT’10), Y. Lechevallier and G. Saporta, Eds. Springer, Paris, France, 177–187.
    4. M. Chai, L. Wang, Y. Weng, X. Jin, and K. Zhou. 2013. Dynamic hair manipulation in images and videos. ACM Trans. Graph. 32, 4 (July), 75:1–75:8. 
    5. Y. Chen, Y. Xu, B. Guo, and H.-Y. Shum. 2002. Modeling and rendering of realistic feathers. ACM Trans. Graph. 21, 3 (July), 630–636. 
    6. M. F. Cohen, J. Shade, S. Hiller, and O. Deussen. 2003. Wang tiles for image and texture generation. ACM Trans. Graph. 22, 3 (July), 287–294. 
    7. E. d’Eon, G. Francois, M. Hill, J. Letteri, and J.-M. Aubry. 2011. An energy-conserving hair reflectance model. Comput. Graph. Forum 30, 4, 1181–1187. 
    8. D. E. Drake and S. Hougardy. 2003. A simple approximation algorithm for the weighted matching problem. Inf. Process. Lett. 85, 211–213. 
    9. I. Gkioulekas, S. Zhao, K. Bala, T. Zickler, and A. Levin. 2013. Inverse volume rendering with material dictionaries. ACM Trans. Graph. 32, 6 (Nov.), 162:1–162:13. 
    10. M. Hašan and R. Ramamoorthi. 2013. Interactive albedo editing in path-traced volumetric materials. ACM Trans. Graph. 32, 2. 
    11. T. L. Herrera, A. Zinke, and A. Weber. 2012. Lighting hair from the inside: A thermal approach to hair reconstruction. ACM Trans. Graph. 31, 6 (Nov.), 146:1–146:9. 
    12. P. Irawan and S. Marschner. 2012. Specular reflection from woven cloth. ACM Trans. Graph. 31, 1 (Feb.), 11:1–11:20. 
    13. W. Jakob, A. Arbree, J. T. Moon, K. Bala, and S. Marschner. 2010. A radiative transfer framework for rendering materials with anisotropic structure. ACM Trans. Graph. 29, 4 (July), 53:1–53:13. 
    14. W. Jakob, J. T. Moon, and S. Marschner. 2009. Capturing hair assemblies fiber by fiber. ACM Trans. Graph. 28, 5 (Dec.), 164:1–164:9. 
    15. J. T. Kajiya and T. L. Kay. 1989. Rendering fur with three dimensional textures. In Proceedings of SIGGRAPH’89. 271–280. 
    16. N. Kang and J. Zhang. 2005. White matter fiber tractography via anisotropic diffusion simulation in the human brain. IEEE Med. Imaging 24, 9 (Sept.), 1127–37.
    17. T. Lindeberg. 1998. Feature detection with automatic scale selection. Int. J. Comput. Vision 30, 79–116. 
    18. L. Luo, H. Li, and S. Rusinkiewicz. 2013. Structure-aware hair capture. ACM Trans. Graph. 32, 4 (July). 
    19. S. R. Marschner, H. W. Jensen, M. Cammarano, S. Worley, and P. Hanrahan. 2003. Light scattering from human hair fibers. In Proceedings of SIGGRAPH’03. 780–791. 
    20. S. Paris, H. Briceño, and F. Sillion. 2004. Capture of hair geometry from multiple images. ACM Trans. Graph. 23, 3 (Aug.), 712–719. 
    21. G. J. Parker, C. A. Wheeler-Kingshott, and G. J. Barker. 2002. Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE Trans. Med. Imaging 21, 5 (May), 505–512.
    22. I. Sadeghi, O. Bisker, J. De Deken, and H. W. Jensen. 2013. A practical microcylinder appearance model for cloth rendering. ACM Trans. Graph. 32, 2 (Apr.), 14:1–14:12. 
    23. M. Sattler, R. Sarlette, and R. Klein. 2003. Efficient and realistic visualization of cloth. In Proceedings of the Eurographics Symposium on Rendering. 
    24. K. Schröder, R. Klein, and A. Zinke. 2011. A volumetric approach to predictive rendering of fabrics. Comput. Graph. Forum 30, 4, 1277–1286. 
    25. K. Schröder, A. Zinke, and R. Klein. 2014. Image-based reverse engineering and visual prototyping of woven cloth. IEEE Trans. Visual. Comput. Graph. PP, 99.
    26. T. Shinohara, J. Ya Takayama, S. Ohyama, and A. Kobayashi. 2010. Extraction of yarn positional information from a three-dimensional CT image of textile fabric using yarn tracing with a filament model for structure analysis. Textile Res. J. 80, 7, 623–630.
    27. K. Sreprateep and E. L. J. Bohez. 2006. Computer aided modeling of fiber assemblies. Comput. Aid. Des. Appl. 3, 1–4, 367–376.
    28. K. Ward, F. Bertails, T. Yong Kim, S. R. Marschner, M. Paule Cani, and M. C. Lin. 2006. A survey on hair modeling: Styling, simulation, and rendering. IEEE Trans. Visual. Comput. Graph. 13, 2, 213–234. 
    29. Y.-Q. Xu, Y. Chen, S. Lin, H. Zhong, E. Wu, B. Guo, and H.-Y. Shum. 2001. Photorealistic rendering of knitwear using the lumislice. In Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’01). ACM, New York, NY, 391–398 
    30. J. Zhang, G. Baciu, D. Zheng, C. Liang, G. Li, and J. Hu. 2013. IDSS: A novel representation for woven fabrics. IEEE Trans. Visual. Comput. Graph. 19, 3, 420–432. 
    31. S. Zhao, W. Jakob, S. Marschner, and K. Bala. 2011. Building volumetric appearance models of fabric using micro CT imaging. ACM Trans. Graph. 30, 44:1–44:10. 
    32. A. Zinke and A. Weber. 2007. Light scattering from filaments. IEEE Trans. Visual. Comput. Graph. 13, 2, 342–356

ACM Digital Library Publication:


Overview Page: