Abstract
To cope with the richness in appearance variation found in real-world data under natural illumination, we propose to synthesize training data capturing these variations for material classification. Using synthetic training data created from separately acquired material and illumination characteristics allows to overcome the problems of existing material databases which only include a tiny fraction of the possible real-world conditions under controlled laboratory environments. However, it is essential to utilize a representation for material appearance which preserves fine details in the reflectance behavior of the digitized materials. As BRDFs are not sufficient for many materials due to the lack of modeling mesoscopic effects, we present a high-quality BTF database with 22,801 densely measured view-light configurations including surface geometry measurements for each of the 84 measured material samples. This representation is used to generate a database of synthesized images depicting the materials under different view-light conditions with their characteristic surface geometry using image-based lighting to simulate the complexity of real-world scenarios. We demonstrate that our synthesized data allows classifying materials under complex real-world scenarios.
Chapter PDF
Similar content being viewed by others
References
(November 2013), http://www.pauldebevec.com/
Barron, J.T., Malik, J.: Shape, albedo, and illumination from a single image of an unknown object. In: CVPR, pp. 334–341 (2012)
Barron, J., Malik, J.: Intrinsic scene properties from a single rgb-d image. In: CVPR, pp. 17–24 (2013)
Ben-Artzi, A., Ramamoorthi, R., Agrawala, M.: Efficient shadows for sampled environment maps. J. Graphics Tools 11(1), 13–36 (2006)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: ICCV, vol. 2, pp. 1597–1604 (2005)
Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real world surfaces. Tech. rep. (1996)
Dana, K.J., Nayar, S.K., Ginneken, B.V., Koenderink, J.J.: Reflectance and texture of real-world surfaces. In: CVPR, pp. 151–157 (1997)
Debevec, P.: Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography. In: SIGGRAPH, pp. 189–198 (1998)
Enzweiler, M., Gavrila, D.M.: A mixed generative-discriminative framework for pedestrian classification. In: CVPR, pp. 1–8 (2008)
Filip, J., Haindl, M.: Bidirectional texture function modeling: A state of the art survey. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1921–1940 (2009)
Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)
Jegou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: CVPR, pp. 3304–3311 (2010)
Kaneva, B., Torralba, A., Freeman, W.T.: Evaluation of image features using a photorealistic virtual world. In: ICCV, pp. 2282–2289 (2011)
Li, W., Fritz, M.: Recognizing materials from virtual examples. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 345–358. Springer, Heidelberg (2012)
Liu, C., Sharan, L., Adelson, E.H., Rosenholtz, R.: Exploring features in a bayesian framework for material recognition. In: CVPR, pp. 239–246 (2010)
Liu, C., Yang, G., Gu, J.: Learning discriminative illumination and filters for raw material classification with optimal projections of bidirectional texture functions. In: CVPR, pp. 1430–1437 (2013)
Oxholm, G., Nishino, K.: Shape and reflectance from natural illumination. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 528–541. Springer, Heidelberg (2012)
Perronnin, F., Dance, C.R.: Fisher kernels on visual vocabularies for image categorization. In: CVPR (2007)
Pishchulin, L., Jain, A., Wojek, C., Andriluka, M., Thormählen, T., Schiele, B.: Learning people detection models from few training samples. In: CVPR, pp. 1473–1480 (2011)
Reeves, W.T., Salesin, D.H., Cook, R.L.: Rendering antialiased shadows with depth maps. SIGGRAPH Comput. Graph. 21(4), 283–291 (1987)
Ruiters, R., Schwartz, C., Klein, R.: Example-based interpolation and synthesis of bidirectional texture functions. Computer Graphics Forum (Proceedings of the Eurographics 2013) 32(2), 361–370 (2013)
Schwartz, C., Weinmann, M., Ruiters, R., Klein, R.: Integrated high-quality acquisition of geometry and appearance for cultural heritage. In: The 12th International Symposium on Virtual Reality, Archeology and Cultural Heritage VAST 2011, pp. 25–32 (2011)
Sharan, L., Rosenholtz, R., Adelson, E.H.: Material perception: What can you see in a brief glance? Journal of Vision 8 (2009)
Sharan, L., Liu, C., Rosenholtz, R., Adelson, E.H.: Recognizing materials using perceptually inspired features. IJCV 103(3), 348–371 (2013)
Shotton, J., Fitzgibbon, A.W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR, pp. 1297–1304 (2011)
Stark, M., Goesele, M., Schiele, B.: Back to the future: Learning shape models from 3d cad data. In: BMVC. pp. 106.1–106.11 (2010)
Targhi, A.T., Geusebroek, J.-M., Zisserman, A.: Texture classification with minimal training images. In: International Conference on Pattern Recognition, pp. 1–4 (2008)
Timofte, R., Van Gool, L.: A training-free classification framework for textures, writers, and materials. In: BMVC, pp. 1–12 (2012)
Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc., New York (1995)
Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)
Wang, J., Dana, K.J.: Hybrid textons: Modeling surfaces with reflectance and geometry. In: CVPR, vol. 1, pp. 372–378.
Wenzel, J.: Mitsuba renderer (2010), http://www.mitsuba-renderer.org
Wong, T.-T., Heng, P.-A., Or, S.-H., Ng, W.-Y.: Image-based rendering with controllable illumination. In: EGWR, pp. 13–22 (1997)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. IJCV 73(2), 213–238 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Weinmann, M., Gall, J., Klein, R. (2014). Material Classification Based on Training Data Synthesized Using a BTF Database. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8691. Springer, Cham. https://doi.org/10.1007/978-3-319-10578-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-10578-9_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10577-2
Online ISBN: 978-3-319-10578-9
eBook Packages: Computer ScienceComputer Science (R0)