Texture map generation for 3D reconstructed scenes

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We present a novel method for generating texture maps for 3D geometric models reconstructed using consumer RGB-D sensors. Our method generates a texture map for a simplified 3D mesh of the reconstructed scene using spatially and temporally sub-sampled key frames of the input RGB stream. We acquire an accurate texture map by optimizing the texture coordinates of the 3D model to maximize the photometric consistency among multiple key frames. We show that the optimization can be performed efficiently using GPU by exploiting the locality of texture coordinate manipulation. Experimental results demonstrate that our method can generate a texture map in a few tens of seconds for a large 3D model, such as a whole room.

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  1. 1.

    Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S.M., Szeliski, R.: Building rome in a day. Commun. ACM 54(10), 105–112 (2011)

  2. 2.

    Blender foundation: blender. Accessed Jan 2016

  3. 3.

    Cignoni, P., Corsini, M., Ranzuglia, G.: MeshLab: an open-source 3D mesh processing system. Ercim News 73(45–46), 6 (2008)

  4. 4.

    Crandall, D., Owens, A., Snavely, N., Huttenlocher, D.: Discrete-continuous optimization for large-scale structure from motion. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3001–3008. IEEE (2011)

  5. 5.

    Crété-Roffet, F., Dolmiere, T., Ladret, P., Nicolas, M.: The blur effect: perception and estimation with a new no-reference perceptual blur metric. In: SPIE Electronic Imaging Symposium Conf Human Vision and Electronic Imaging, vol. 12, pp. EI–6492 (2007)

  6. 6.

    Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 209–216. ACM Press/Addison-Wesley Publishing Co., New York (1997)

  7. 7.

    Hazewinkel, M. (ed.): Encyclopaedia of Mathematics (Set). Springer, The Netherlands (1994)

  8. 8.

    Lévy, B., Petitjean, S., Ray, N., Maillot, J.: Least squares conformal maps for automatic texture atlas generation. ACM Trans. Graph. (TOG) 21(3), 362–371 (2002)

  9. 9.

    Liu, L., Zhang, L., Xu, Y., Gotsman, C., Gortler, S.J.: A local/global approach to mesh parameterization. In: Computer Graphics Forum, vol. 27, pp. 1495–1504. Wiley Online Library, New York (2008)

  10. 10.

    Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136. IEEE (2011)

  11. 11.

    Niener, M., Zollhfer, M., Izadi, S., Stamminger, M.: Real-time 3D reconstruction at scale using voxel hashing. ACM Trans. Graph. (TOG) 32(6), 169 (2013)

  12. 12.

    Roth, H., Vona, M.: Moving volume kinectfusion. In: BMVC, pp. 1–11 (2012)

  13. 13.

    Smith, J., Schaefer, S.: Bijective parameterization with free boundaries. ACM Trans. Graph. (TOG) 34(4), 70 (2015)

  14. 14.

    Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. Int. J. Comput. Vis. 9(2), 137–154 (1992)

  15. 15.

    Whelan, T., Johannsson, H., Kaess, M., Leonard, J.J., McDonald, J.: Robust tracking for real-time dense rgb-d mapping with kintinuous. Technical Report MIT-CSAIL-TR-2012-031, CSAIL, MIT (2012)

  16. 16.

    Zhou, Q.Y., Koltun, V.: Dense scene reconstruction with points of interest. ACM Trans. Graph. (TOG) 32(4), 112 (2013)

  17. 17.

    Zhou, Q.Y., Koltun, V.: Color map optimization for 3D reconstruction with consumer depth cameras. ACM Trans. Graph. (TOG) 33(4), 155 (2014)

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This work was supported by the National Research Foundation of Korea (NRF) Grant (NRF-2014R1A2A1A11052779) and Institute for Information and Communications Technology Promotion (IITP) Grant (R0126-16-1078), both funded by the Korea government (MSIP).

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Correspondence to Seungyong Lee.

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Jeon, J., Jung, Y., Kim, H. et al. Texture map generation for 3D reconstructed scenes. Vis Comput 32, 955–965 (2016).

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  • 3D reconstruction
  • Texture mapping
  • RGB-D images
  • Photometric consistency optimization