Hybrid Haptic Texture Rendering Using Kinesthetic and Vibrotactile Feedback

  • Sunghwan Shin
  • Seungmoon Choi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 432)


We present a hybrid haptic texture rendering framework for inhomogeneous texture with high realism. The micro-geometry of a real texture sample is captured using photometric stereo and then rendered using a force feedback device. The vibrational response of the texture is expressed using a neural network-based data-driven model and re-created using a vibration actuator. The former represents the position-dependent geometric property while the latter delivers the invariant aspects including material properties. Our hybrid texture rendering system can improve the realism of virtual haptic texture rendering considerably, although formal verification awaits.


Haptic texture Rendering Photometric stereo Data-driven model Inhomogeneous Synthesis 



This work was supported in part by the Dual Use Technology Center Program funded by the Ministry of Trade, Industry and Energy and Defense Acquisition Program Administration (12-DU-EE-03), and the Pioneer Research Center Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (2011-0027994).


  1. 1.
    Minsky, M.: Computational Haptics: The Sandpaper System for Synthesizing Texture for a Force-Feedback Display. Massachusetts Institute of Technology (1995)Google Scholar
  2. 2.
    Romano, J., Kuchenbecker, K.: Creating realistic virtual textures from contact acceleration data. IEEE Trans. Haptics 5(2), 109–119 (2012)CrossRefGoogle Scholar
  3. 3.
    Culbertson, H., Unwin, J., Goodman, B., Kuchenbecker, K.: Generating haptic texture models from unconstrained tool-surface interactions. In: IEEE Wrold Haptics Conference, pp. 295–300. IEEE Press, Korea (2013)Google Scholar
  4. 4.
    Shin, S., Osgouei, R.H., Kim, K.-D., Choi, S.: Data-driven modeling of isotropic haptic textures using frequency-decomposed neural networks. In: IEEE World Haptics Conference, pp. 131–138. IEEE Press, Chicago (2015)Google Scholar
  5. 5.
    Hollins, M., Risner, S.: Evidence for the duplex theory of tactile texture perception. Percept. Psychophys. 62(4), 695–705 (2000)CrossRefGoogle Scholar
  6. 6.
    Paterson, J., Claus, D., Fitzgibbon, A.: BRDF and geometry capture from extended inhomogeneous samples using flash photography. Comput. Graph. Forum 24(3), 383–391 (2005)CrossRefGoogle Scholar
  7. 7.
    Shin, S., Choi, S.: Haptic texture modeling using photometric stereo. In: IEEE Haptics Symposium, pp. 366–368. IEEE Press, Philadelphia (2016)Google Scholar
  8. 8.
    Choi, S., Tan, H.Z.: Towards realistic haptic rendering of surface texture. IEEE Comput. Graph. Appl. 24, 40–47 (2004)CrossRefGoogle Scholar
  9. 9.
    Choi, S., Walker, L., Tan, H.Z., Crittenden, S., Reifenberger, R.: Force constancy and its effect on haptic perception of virtual surfaces. ACM Trans. Appl. Percept. 2(2), 89–105 (2005)CrossRefGoogle Scholar
  10. 10.
    Campion, G.: The Synthesis of Three Dimensional Haptic Textures. Springer, London (2011)zbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Pohang University of Science and Technology (POSTECH)PohangRepublic of Korea

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