Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets

  • Burkhard Güssefeld
  • Katrin Honauer
  • Daniel Kondermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)


Optical flow ground truth generated by computer graphics has many advantages. For example, we can systematically vary scene parameters to understand algorithm sensitivities. But is synthetic ground truth realistic enough? Appropriate material models have been established as one of the major challenges for the creation of synthetic datasets: previous research has shown that highly sophisticated reflectance field acquisition methods yield results, which various optical flow methods cannot distinguish from real scenes. However, such methods are costly both in acquisition and rendering time and thus infeasible for large datasets. In this paper we find the simplest reflectance models (RM) for different groups of materials which still provide sufficient accuracy for optical flow performance analysis. It turns out that a spatially varying Phong RM is sufficient for simple materials. Normal estimation combined with Anisotropic RM can handle even very complex materials.


  1. 1.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)Google Scholar
  2. 2.
    Kondermann, D., Nair, R., Meister, S., Mischler, W., Güssefeld, B., Honauer, K., Hofmann, S., Brenner, C., Jähne, B.: Stereo ground truth with error bars. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9007, pp. 595–610. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-16814-2_39 Google Scholar
  3. 3.
    Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 611–625. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33783-3_44 CrossRefGoogle Scholar
  4. 4.
    Mac Aodha, O., Humayun, A., Pollefeys, M., Brostow, G.J.: Learning a confidence measure for optical flow. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1107–1120 (2012)CrossRefGoogle Scholar
  5. 5.
    Vaudrey, T., Rabe, C., Klette, R., Milburn, J.: Differences between stereo and motion behaviour on synthetic and real-world stereo sequences. In: Proceedings of the 23rd International Conference on Image and Vision Computing New Zealand (2008)Google Scholar
  6. 6.
    Meister, S., Kondermann, D.: Real versus realistically rendered scenes for optical flow evaluation. In: ITG Conference on Electronic Media Technology (2011)Google Scholar
  7. 7.
    Schwartz, C., Sarlette, R., Weinmann, M., Klein, R.: Dome II: a parallelized BTF acquisition system. In: Eurographics Workshop on Material Appearance Modeling: Issues and Acquisition, pp. 25–31. Eurographics Association (2013)Google Scholar
  8. 8.
    Güssefeld, B., Kondermann, D., Schwartz, C., Klein, R.: Are reflectance field renderings appropriate for optical flow evaluation? In: IEEE International Conference on Image Processing (ICIP), Paris, France. IEEE (2014)Google Scholar
  9. 9.
    Heeger, D.: Model for the extraction of image flow. J. Opt. Soc, Am. 4, 1455–1471 (1987)CrossRefGoogle Scholar
  10. 10.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92, 1–31 (2011)CrossRefGoogle Scholar
  11. 11.
    Martull, S., Peris, M., Fukui, K.: Realistic CG stereo image dataset with ground truth disparity maps. In: 2012 21st International Conference on Proceedings of The 3rd International Workshop on Benchmark Test Schemes for AR/MR Geometric Registration and Tracking Method (TrakMark2012). Pattern Recognition (ICPR) (2012)Google Scholar
  12. 12.
    Haltakov, V., Unger, C., Ilic, S.: Framework for generation of synthetic ground truth data for driver assistance applications. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 323–332. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40602-7_35 CrossRefGoogle Scholar
  13. 13.
    Haeusler, R., Kondermann, D.: Synthesizing real world stereo challenges. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 164–173. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40602-7_17 CrossRefGoogle Scholar
  14. 14.
    Nicodemus, F.E.: Directional reflectance and emissivity of an opaque surface. Appl. Opt. 4, 767–775 (1965)CrossRefGoogle Scholar
  15. 15.
    Matusik, W., Pfister, H., Brand, M., McMillan, L.: A data-driven reflectance model. ACM Trans. Graph. 22, 759–769 (2003)CrossRefGoogle Scholar
  16. 16.
    Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Trans. Graph. 18, 1–34 (1999)CrossRefGoogle Scholar
  17. 17.
    Weinmann, M., Gall, J., Klein, R.: Material classification based on training data synthesized using a BTF database. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 156–171. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-10578-9_11 Google Scholar
  18. 18.
    Phong, B.T.: Illumination for computer generated pictures. Commun. ACM 18, 311–317 (1975)CrossRefGoogle Scholar
  19. 19.
    Ward, G.J.: Measuring and modeling anisotropic reflection. SIGGRAPH Comput. Graph. 26, 265–272 (1992)CrossRefGoogle Scholar
  20. 20.
    Cook, R.L., Torrance, K.E.: A reflectance model for computer graphics. ACM Trans. Graph. 1, 7–24 (1982)CrossRefGoogle Scholar
  21. 21.
    Ngan, A., Durand, F., Matusik, W.: Experimental analysis of BRDF models. In: Proceedings of the Eurographics Symposium on Rendering, pp. 117–226. Eurographics Association (2005)Google Scholar
  22. 22.
    Lafortune, E.P., Willems, Y.D.: Using the modified phong reflectance model for physically based rendering. Technical report (1994)Google Scholar
  23. 23.
    Geisler-Moroder, D., Dür, A.: A new ward BRDF model with bounded albedo. Comput. Graph. Forum 29, 1391–1398 (2010)CrossRefGoogle Scholar
  24. 24.
    Ashikhmin, M., Shirley, P.: An anisotropic phong BRDF model. J. Graph. Tools 5, 25–32 (2000)CrossRefGoogle Scholar
  25. 25.
    Prados, E., Faugeras, O.: Shape from shading. In: Paragios, N., Chen, Y., Faugeras, O. (eds.) Handbook of Mathematical Models in Computer Vision, pp. 375–388. Springer, New York (2006)Google Scholar
  26. 26.
    Hart, J.C.: Perlin noise pixel shaders. In: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Workshop on Graphics Hardware, HWWS 2001, pp. 87–94. ACM, New York (2001)Google Scholar
  27. 27.
    Xu, L., Jia, J., Matsushita, Y.: Motion detail preserving optical flow estimation. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1744–1757 (2012)CrossRefGoogle Scholar
  28. 28.
    Horn, D.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)CrossRefGoogle Scholar
  29. 29.
    Gottfried, J., Kondermann, D.: Charon suite software framework. Image Processing Online (IPOL) (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Burkhard Güssefeld
    • 1
  • Katrin Honauer
    • 1
  • Daniel Kondermann
    • 1
  1. 1.Heidelberg Collaboratory for Image ProcessingUniversity of HeidelbergHeidelbergGermany

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