Efficient Reconstruction of Complex 3-D Scenes from Incomplete RGB-D Data

  • Sergio A. Mota-Gutierrez
  • Jean-Bernard Hayet
  • Salvador Ruiz-Correa
  • Rogelio Hasimoto-Beltran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8334)

Abstract

In this paper we develop a new approach for reconstructing 3-D scenes from RGB-D data. We use a Markov random field to model appearance relations and geometric cues between different regions of a scene, as a means to provide robustness to noisy and incomplete data often generated by RGB-D devices. A parametric reconstruction of 3-D scenes that enable coherent physical interaction are computed, in near real time, with a standard computer that does not use specialized hardware.

Keywords

Graphical Processing Unit Augmented Reality Image Segment Kinect Sensor Structure From Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Davison, A.J., Reid, I., Molton, N., Stasse, O.: MonoSLAM: Real-Time Single Camera SLAM. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(6), 1052–1067 (2007)CrossRefGoogle Scholar
  2. 2.
    Klein, G., Murray, D.: Parallel Tracking and Mapping for Small AR Workspaces. In: Proc. of the IEEE and ACM Int. Symp. on Mixed and Augmented Reality (2007)Google Scholar
  3. 3.
    Newcombe, R., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: Real-Time Dense Surface Mapping and Tracking. In: Proc. of the IEEE Int. Symp. on Mixed and Augmented Reality (2011)Google Scholar
  4. 4.
    Newcombe, R., Lovegrove, S., Davison, A.J.: DTAM: Dense Tracking and Mapping in Real-Time. In: Proc. of the IEEE Int. Conf. on Computer Vision (2011)Google Scholar
  5. 5.
    Karlsson, N., Di Bernardo, E., Ostrowski, J., Goncalves, L., Pirjanian, P., Munich, M.E.: The vSLAM Algorithm for Robust Localization and Mapping. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 24–29 (2005)Google Scholar
  6. 6.
    Hödlmoser, M., Micusik, B., Kampel, M.: Sparse Point Cloud Densification by Using Redundant Semantic Information. In: Proc. of the Int. Conf. on 3D Vision (2013)Google Scholar
  7. 7.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building Rome in a Day. In: Proc. of IEEE Int. Conf. on Computer Vision (2009)Google Scholar
  8. 8.
    Saxena, A., Sun, M., Ng, A.: Make3D: Learning 3D Scene Structure from a Single Still image. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(5), 824–840 (2009)CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. Int. Journal of Computer Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  10. 10.
    Zhang, Z.: A Flexible New Technique for Camera Calibration. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  11. 11.
    Daniel Herrera, C., Kannala, J., Heikkilä, J.: Joint depth and color camera calibration with distortion correction. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(10), 2058–2064 (2012)CrossRefGoogle Scholar
  12. 12.
    Inc. Free Software Foundation. GLPK (GNU Linear Programming Kit) (2012)Google Scholar
  13. 13.
    Laws, K.: Rapid Texture Identification. In: Proc. SPIE Image Processing for Missile Guidance, pp. 376–381 (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sergio A. Mota-Gutierrez
    • 1
  • Jean-Bernard Hayet
    • 1
  • Salvador Ruiz-Correa
    • 1
  • Rogelio Hasimoto-Beltran
    • 1
  1. 1.Computer Science DepartmentCenter for Research in MathematicsGuanajuatoMéxico

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