Advertisement

Environment Estimation for Glossy Reflections in Mixed Reality Applications Using a Neural Network

  • Tobias SchwandtEmail author
  • Christian Kunert
  • Wolfgang Broll
Chapter
  • 22 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12060)

Abstract

Environment textures are used for the illumination of virtual objects within a virtual scene. Using these textures is crucial for high-quality lighting and reflection. In the case of an augmented reality context, the lighting is very important to seamlessly embed a virtual object within the real world scene. To ensure this, the lighting of the environment has to be captured according to the current light information. In this paper, we present a novel approach by stitching the current camera information onto a cube map. This cube map is enhanced in every single frame and is fed into a neural network to estimate missing parts. Finally, the output of the neural network and the currently stitched information is fused to make even mirror-like reflections possible on mobile devices. We provide an image stream stitching approach combined with a neural network to create plausible and high-quality environment textures that may be used for image-based lighting within mixed reality environments.

Keywords

Augmented reality Enhanced reality Visualization Image fusion Computer vision Neural networks 

Notes

Acknowledgment

The underlying research of these results has been partially funded by the Free State of Thuringia with the number 2015 FE 9108 and co-financed by the European Union as part of the European Regional Development Fund (ERDF).

References

  1. 1.
    Badra, F., Qumsieh, A., Dudek, G.: Rotation and zooming in image mosaicing. In: Proceedings Fourth IEEE Workshop on Applications of Computer Vision, WACV 1998. Institute of Electrical and Electronics Engineers (IEEE) (1998).  https://doi.org/10.1109/acv.1998.732857
  2. 2.
    Chew, V.C.S., Lian, F.L.: Panorama stitching using overlap area weighted image plane projection and dynamic programming for visual localization. In: 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 250–255. Institute of Electrical and Electronics Engineers (IEEE), July 2012.  https://doi.org/10.1109/AIM.2012.6265995
  3. 3.
    Dasgupta, S., Banerjee, A.: An augmented-reality-based real-time panoramic vision system for autonomous navigation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 36(1), 154–161 (2006).  https://doi.org/10.1109/TSMCA.2005.859177CrossRefGoogle Scholar
  4. 4.
    Debevec, P.: Rendering synthetic objects into real scenes. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH 1998, pp. 189–198. Association for Computing Machinery (ACM) (1998).  https://doi.org/10.1145/280814.280864
  5. 5.
    Franke, T.A.: Delta voxel cone tracing. In: 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 39–44. Institute of Electrical & Electronics Engineers (IEEE), September 2014.  https://doi.org/10.1109/ISMAR.2014.6948407
  6. 6.
    Gardner, M.A., et al.: Learning to predict indoor illumination from a single image. ACM Trans. Graph. 36(6), 176:1–176:14 (2017).  https://doi.org/10.1145/3130800.3130891. http://doi.acm.org/10.1145/3130800.3130891CrossRefGoogle Scholar
  7. 7.
    Georgoulis, S., Rematas, K., Ritschel, T., Fritz, M., Tuytelaars, T., Gool, L.V.: What is around the camera? In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22–29 October 2017, pp. 5180–5188. IEEE Computer Society (2017).  https://doi.org/10.1109/ICCV.2017.553
  8. 8.
    Gruber, L., Ventura, J., Schmalstieg, D.: Image-space illumination for augmented reality in dynamic environments. In: 2015 IEEE Virtual Reality (VR), pp. 127–134. Institute of Electrical & Electronics Engineers (IEEE), March 2015.  https://doi.org/10.1109/VR.2015.7223334
  9. 9.
    Iorns, T., Rhee, T.: Real-time image based lighting for 360-degree panoramic video. In: Huang, F., Sugimoto, A. (eds.) PSIVT 2015. LNCS, vol. 9555, pp. 139–151. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-30285-0_12CrossRefGoogle Scholar
  10. 10.
    Kale, P., Singh, K.R.: A technical analysis of image stitching algorithm. Int. J. Comput. Sci. Inf. Technol. 6(1), 284–288 (2015)Google Scholar
  11. 11.
    Kán, P., Unterguggenberger, J., Kaufmann, H.: High-quality consistent illumination in mobile augmented reality by radiance convolution on the GPU. In: Bebis, G., et al. (eds.) ISVC 2015. LNCS, vol. 9474, pp. 574–585. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27857-5_52CrossRefGoogle Scholar
  12. 12.
    Karsch, K., et al.: Automatic scene inference for 3D object compositing. ACM Trans. Graph. 33(3), 1–15 (2014).  https://doi.org/10.1145/2602146CrossRefzbMATHGoogle Scholar
  13. 13.
    Kilbride, S., Kim, M.D., Ueda, J.: Real time image de-blurring and image stitching for muscle inspired camera orientation system. In: 2014 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), pp. 82–87 (2014)Google Scholar
  14. 14.
    Křivánek, J., Colbert, M.: Real-time shading with filtered importance sampling. Comput. Graph. Forum 27(4), 1147–1154 (2008).  https://doi.org/10.1111/j.1467-8659.2008.01252.xCrossRefGoogle Scholar
  15. 15.
    Liao, W.-S., et al.: Real-time spherical panorama image stitching using OpenCL. In: International Conference on Computer Graphics and Virtual Reality, Las Vegas, America, July 2011Google Scholar
  16. 16.
    Mandl, D., et al.: Learning lightprobes for mixed reality illumination. In: 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Institute of Electrical and Electronics Engineers (IEEE) (2017)Google Scholar
  17. 17.
    Mann, S., Picard, R.W.: Virtual bellows: constructing high quality stills from video. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 1994, vol. 1, pp. 363–367. IEEE (1994)Google Scholar
  18. 18.
    Mistry, S., Patel, A.: Image stitching using Harris feature detection. Int. Res. J. Eng. Technol. (IRJET) 03(04), 2220–2226 (2016)Google Scholar
  19. 19.
    Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. CoRR abs/1604.07379 (2016). http://arxiv.org/abs/1604.07379
  20. 20.
    Pravenaa, S., Menaka, R.: A methodical review on image stitching and video stitching techniques. Int. J. Appl. Eng. Res. 11(5), 3442–3448 (2016)Google Scholar
  21. 21.
    Richter-Trummer, T., Kalkofen, D., Park, J., Schmalstieg, D.: Instant mixed reality lighting from casual scanning. In: 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 27–36. Institute of Electrical and Electronics Engineers (IEEE), September 2016.  https://doi.org/10.1109/ISMAR.2016.18
  22. 22.
    Rohmer, K., Buschel, W., Dachselt, R., Grosch, T.: Interactive near-field illumination for photorealistic augmented reality on mobile devices. In: 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 29–38. Institute of Electrical & Electronics Engineers (IEEE), September 2014.  https://doi.org/10.1109/ISMAR.2014.6948406
  23. 23.
    Ropinski, T., Wachenfeld, S., Hinrichs, K.: Virtual reflections for augmented reality environments. In: International Conference on Artificial Reality and Telexistence, pp. 311–318 (2004)Google Scholar
  24. 24.
    Schwandt, T., Broll, W.: A single camera image based approach for glossy reflections in mixed reality applications. In: 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 37–43. Institute of Electrical and Electronics Engineers (IEEE), September 2016.  https://doi.org/10.1109/ISMAR.2016.12
  25. 25.
    Schwandt, T., Broll, W.: Differential G-Buffer rendering for mediated reality applications. In: De Paolis, L.T., Bourdot, P., Mongelli, A. (eds.) AVR 2017. LNCS, vol. 10325, pp. 337–349. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60928-7_30CrossRefGoogle Scholar
  26. 26.
    Schwandt, T., Kunert, C., Broll, W.: Glossy reflections for mixed reality environments on mobile devices. In: Cyberworlds 2018. Institute of Electrical and Electronics Engineers (IEEE) (2018).  https://doi.org/10.1007/978-3-319-60928-7_30
  27. 27.
    Sébastien, L., Zanuttini, A.: Local image-based lighting with parallax-corrected cubemaps. In: ACM SIGGRAPH 2012 Talks, SIGGRAPH 2012, p. 36:1. ACM, New York (2012).  https://doi.org/10.1145/2343045.2343094. http://doi.acm.org/10.1145/2343045.2343094
  28. 28.
    State, A., Hirota, G., Chen, D.T., Garrett, W.F., Livingston, M.A.: Superior augmented reality registration by integrating landmark tracking and magnetic tracking. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH 1996, pp. 429–438. Association for Computing Machinery (ACM) (1996).  https://doi.org/10.1145/237170.237282
  29. 29.
    Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trend\(\textregistered \) Comput. Graph. Vis. 2(1), 1–104 (2006).  https://doi.org/10.1561/0600000009. http://www.nowpublishers.com/product.aspx?product=CGV&doi=0600000009
  30. 30.
    Xiao, J., Ehinger, K.A., Oliva, A., Torralba, A.: Recognizing scene viewpoint using panoramic place representation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2695–2702, June 2012.  https://doi.org/10.1109/CVPR.2012.6247991
  31. 31.
    Yao, X., Zhou, Y., Hu, X., Yang, B.: A new environment mapping method using equirectangular panorama from unordered images. In: 2011 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, pp. 82010V–82010V-9. SPIE-International Society for Optical Engineering, November 2011.  https://doi.org/10.1117/12.904704

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Tobias Schwandt
    • 1
    Email author
  • Christian Kunert
    • 1
  • Wolfgang Broll
    • 1
  1. 1.Ilmenau University of TechnologyIlmenauGermany

Personalised recommendations