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The Visual Computer

, Volume 34, Issue 3, pp 377–389 | Cite as

3D reconstruction framework via combining one 3D scanner and multiple stereo trackers

  • Jinlong Shi
  • Zhengxing Sun
  • Suqin Bai
Original Article
  • 278 Downloads

Abstract

This paper presents a novel 3D reconstruction framework of large objects, where we adopt one 3D scanner to reconstruct partial sections of large objects, and employ multiple stereo trackers to extend reconstruction range. Both the 3D scanner and stereo trackers are fitted with infrared light-emitting diode (LED) lights. During reconstruction, the stereo trackers are placed one after another, their poses are estimated according to the LED lights, the 3D scanner is moved to reconstruct partial sections of a large object, and the LED lights on the 3D scanner are tracked by the stereo trackers to compute the poses of the 3D scanner for partial alignment. The experimental results show that this proposed method can accurately and effectively reconstruct large objects, and has its advantages for long-range reconstruction compared with similar existing methods.

Keywords

Large 3D Reconstruction Stereo Tracker 

Notes

Acknowledgements

This work is supported by General Financial Grant from the China Postdoctoral Science Foundation No. 2014M560417; the National Natural Science Foundation of China Nos. 61272219, 61100110, 61321491; the National High Technology Research and Development Program of China No. 2007AA01Z334; the Key Projects Innovation Fund of State Key Laboratory No. ZZKT2013A12; the Program for New Century Excellent Talents in University of China No. NCET-04-04605; the Graduate Training Innovative Projects Foundation of Jiangsu Province No. CXLX13 050; the Science and Technology Program of Jiangsu Province Nos. BE2010072, BE2011058, BY2012190.

References

  1. 1.
    Komodakis, N., Tziritas, G.: Real-time exploration and photorealistic reconstruction of large natural environments. Vis. Comput. 25(2), 117–137 (2009)CrossRefGoogle Scholar
  2. 2.
    Zhu, C., Leow, W.K.: Textured mesh surface reconstruction of large buildings with multi-view stereo. Vis. Comput. 29(6–8), 609–615 (2013)CrossRefGoogle Scholar
  3. 3.
    Shi, J., Zou, D., Bai, S., Qian, Q., Pang, L.: Reconstruction of dense three-dimensional shapes for outdoor scenes from an image sequence. Opt. Eng. 52(12), 123104–123104 (2013)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Towards internet-scale multi-view stereo. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 1434–1441 (2010)Google Scholar
  6. 6.
    Shan, Q., Adams, R., Curless, B., Furukawa, Y., Seitz, S.M.: The visual turing test for scene reconstruction. In: 2013 International Conference on 3DTV-Conference, IEEE, pp. 25–32 (2013)Google Scholar
  7. 7.
    Xiao, J., Furukawa, Y.: Reconstructing the worlds museums. Int. J. Comput. Vis. 110(3), 243–258 (2014)CrossRefGoogle Scholar
  8. 8.
    Jeon, J., Jung, Y., Kim, H., Lee, S.: Texture map generation for 3D reconstructed scenes. Vis. Comput. 32(6), 955–965 (2016)Google Scholar
  9. 9.
    Kurazume, R., Tobata, Y., Iwashita, Y., Hasegawa, T.: 3D laser measurement system for large scale architectures using multiple mobile robots. In: Sixth International Conference on 3-D Digital Imaging and Modeling, 3DIM’07, IEEE, pp. 91–98 (2007)Google Scholar
  10. 10.
    Shim, H., Adelsberger, R., Kim, J.D., Rhee, S.-M., Rhee, T., Sim, J.-Y., Gross, M., Kim, C.: Time-of-flight sensor and color camera calibration for multi-view acquisition. Vis. Comput. 28(12), 1139–1151 (2012)CrossRefGoogle Scholar
  11. 11.
    Iddan, G., Yahav, G.: Three-dimensional imaging in the studio and elsewhere. In: Photonics West 2001-Electronic Imaging, International Society for Optics and Photonics, pp. 48–55 (2001)Google Scholar
  12. 12.
    Yahav, G., Iddan, G., Mandelboum, D.: 3D imaging camera for gaming application. In: International Conference on Consumer Electronics, 2007. ICCE 2007. Digest of Technical Papers, IEEE, pp. 1–2 (2007)Google Scholar
  13. 13.
    Schuon, S., Theobalt, C., Davis, J., Thrun, S.: Lidarboost: depth superresolution for tof 3d shape scanning. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, IEEE, pp. 343–350 (2009)Google Scholar
  14. 14.
    Cui, Y., Schuon, S., Chan, D., Thrun, S., Theobalt, C.: 3d shape scanning with a time-of-flight camera. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 1173–1180 (2010)Google Scholar
  15. 15.
    Song, X., Zhong, F., Wang, Y., Qin, X.: Estimation of kinect depth confidence through self-training. Vis. Comput. 30(6–8), 855–865 (2014)CrossRefGoogle Scholar
  16. 16.
    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), IEEE, pp. 127–136 (2011)Google Scholar
  17. 17.
    Izadi, S., Kim, D.: Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th annual ACM symposium on User interface software and technology, ACM, pp. 559–568 (2011)Google Scholar
  18. 18.
    Chen, J., Bautembach, D., Izadi, S.: Scalable real-time volumetric surface reconstruction. ACM Trans. Graph. (TOG) 32(4), 113 (2013)MATHGoogle Scholar
  19. 19.
    Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using kinect-style depth cameras for dense 3d modeling of indoor environments. Int. J. Robot. Res. 31(5), 647–663 (2012)CrossRefGoogle Scholar
  20. 20.
    Bylow, E., Sturm, J., Kerl, C., Kahl, F., Cremers, D.: Real-time camera tracking and 3d reconstruction using signed distance functions. In: Robotics: Science and Systems (RSS) Conference 2013, vol. 9 (2013)Google Scholar
  21. 21.
    Barone, S., Paoli, A., Razionale, A.V.: Three-dimensional point cloud alignment detecting fiducial markers by structured light stereo imaging. Mach. Vis. Appl. 23(2), 217–229 (2012)CrossRefGoogle Scholar
  22. 22.
    Paoli, A., Razionale, A.V.: Large yacht hull measurement by integrating optical scanning with mechanical tracking-based methodologies. Robot. Comput. Integr. Manuf. 28(5), 592–601 (2012)CrossRefGoogle Scholar
  23. 23.
    Shi, J., Sun, Z., Bai, S.: Large-scale three-dimensional measurement via combining 3d scanner and laser rangefinder. Appl. Opt. 54(10), 2814–2823 (2015)CrossRefGoogle Scholar
  24. 24.
    Shi, J., Sun, Z.: Large-scale three-dimensional measurement based on LED marker tracking. Vis. Comput. 32(2), 179–190 (2016)Google Scholar
  25. 25.
    Barone, S., Paoli, A., Viviano, A.: Razionale, shape measurement by a multi-view methodology based on the remote tracking of a 3d optical scanner. Opt. Lasers Eng. 50(3), 380–390 (2012)CrossRefGoogle Scholar
  26. 26.
    Lucas, B.D., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision. IJCAI 81, 674–679 (1981)Google Scholar
  27. 27.
    Tomasi, C., Kanade, T.: Detection and Tracking of Point Features, School of Computer Science. Carnegie Mellon University, Pittsburgh (1991)Google Scholar
  28. 28.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2003)MATHGoogle Scholar
  29. 29.
    Stringa, E., Regazzoni, C.S.: Real-time video-shot detection for scene surveillance applications. IEEE Trans. Image Process. 9(1), 69–79 (2000)CrossRefGoogle Scholar
  30. 30.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  2. 2.School of Computer Science and EngineeringJiangsu University of Science and TechnologyZhenjiangPeople’s Republic of China

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