Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Komodakis, N., Tziritas, G.: Real-time exploration and photorealistic reconstruction of large natural environments. Vis. Comput. 25(2), 117–137 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  7. Xiao, J., Furukawa, Y.: Reconstructing the worlds museums. Int. J. Comput. Vis. 110(3), 243–258 (2014)

    Article  Google Scholar 

  8. Jeon, J., Jung, Y., Kim, H., Lee, S.: Texture map generation for 3D reconstructed scenes. Vis. Comput. 32(6), 955–965 (2016)

  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)

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

  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)

    Article  Google Scholar 

  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)

  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)

  18. Chen, J., Bautembach, D., Izadi, S.: Scalable real-time volumetric surface reconstruction. ACM Trans. Graph. (TOG) 32(4), 113 (2013)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  24. Shi, J., Sun, Z.: Large-scale three-dimensional measurement based on LED marker tracking. Vis. Comput. 32(2), 179–190 (2016)

  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)

    Article  Google Scholar 

  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. Tomasi, C., Kanade, T.: Detection and Tracking of Point Features, School of Computer Science. Carnegie Mellon University, Pittsburgh (1991)

    Google Scholar 

  28. Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  29. Stringa, E., Regazzoni, C.S.: Real-time video-shot detection for scene surveillance applications. IEEE Trans. Image Process. 9(1), 69–79 (2000)

    Article  Google Scholar 

  30. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China

    Jinlong Shi & Zhengxing Sun

  2. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, People’s Republic of China

    Jinlong Shi & Suqin Bai

Authors
  1. Jinlong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengxing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suqin Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengxing Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, J., Sun, Z. & Bai, S. 3D reconstruction framework via combining one 3D scanner and multiple stereo trackers. Vis Comput 34, 377–389 (2018). https://doi.org/10.1007/s00371-016-1339-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-016-1339-4

Keywords

Search

Navigation