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A self-calibrated photo-geometric depth camera

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Abstract

Compared with geometric stereo vision based on triangulation principle, photometric stereo method has advantages in recovering per-pixel surface details. In this paper, we present a practical 3D imaging system by combining the near-light photometric stereo and the speckle-based stereo matching method. The system is compact in structure and suitable for multi-albedo targets. The parameters (including position and intensity) of the light sources can be self-calibrated. To realize the auto-calibration, we first use the distant lighting model to estimate the initial surface albedo map, and then with the estimated albedo map and the normal vector field fixed, the parameters of the near lighting model are optimized. Next, with the optimized lighting model, we use the near-light photometric stereo method to re-compute the surface normal and fuse it with the coarse depth map from stereo vision to achieve high-quality depth map. Experimental results show that our system can realize high-quality reconstruction in general indoor environments.

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References

  1. Blais, F.: Review of 20 years of range sensor development. J. Electron. Imaging 13(1), 231–243 (2004)

    Article  Google Scholar 

  2. Zhang, S., Van Der Weide, D., Oliver, J.: Superfast phase-shifting method for 3-D shape measurement. Opt. Express 18(9), 9684–9689 (2010)

    Article  Google Scholar 

  3. Jiang, C., Bell, T., Zhang, S.: High dynamic range real-time 3D shape measurement. Opt. Express 24(7), 7337–7346 (2016)

    Article  Google Scholar 

  4. Kinect. http://www.xbox.com

  5. Newcombe, R.A., Izadi, S., Hilliges, O., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. In: Proceedings of IEEE International Symposium on Mixed and Augmented Reality. IEEE, pp. 127–136 (2011)

  6. Haque, M., Chatterjee, A., Govindu, V. Madhav.: High quality photometric reconstruction using a depth camera. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 2275–2282 (2014)

  7. Chatterjee, A., Govindu, V.M.: Photometric refinement of depth maps for multi-albedo objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 933–941 (2015)

  8. Han, Y., Lee, J.Y., Kweon, I.S.: High quality shape from a single RGB-D image under uncalibrated natural illumination. In: Proceedings of IEEE International Conference on Computer Vision. IEEE, pp. 1617–1624 (2013)

  9. Nehab, D., Rusinkiewicz, S., Davis, J., Ramamoorthi, R.: Efficiently combining positions and normals for precise 3D geometry. ACM Trans. Graph. 24(3), 536–543 (2005)

    Article  Google Scholar 

  10. Quau, Y., Mecca, R., Durou, J.D.: Unbiased photometric stereo for colored surfaces: a variational approach. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 4359–4368 (2016)

  11. Higo, T., Matsushita, Y., Joshi, N., Ikeuchi, K.: A hand-held photometric stereo camera for 3-d modeling. In: Proceedings of IEEE International Conference on Computer Vision. IEEE, pp. 1234–1241 (2009)

  12. Wang, C., Wang, L., Matsushita, Y.: Binocular photometric stereo acquisition and reconstruction for 3d talking head applications. In: Proceedings of International Speech Communication Association, pp. 2748–2752 (2013)

  13. Shi, B., Inose, K., Matsushita, Y., Tan, P., Yeung, S., Ikeuchi, K.: Photometric stereo using internet images. In: Proceedings of International Conference on 3D Vision (3DV) (2014)

  14. Powell, M.W., Sarkar, S., Goldgof, D.: A simple strategy for calibrating the geometry of light sources. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 1022–1027 (2001)

    Article  Google Scholar 

  15. Zhou, W., Kambhamettu, C.: Estimation of illuminant direction and intensity of multiple light sources. In: Proceedings of European Conference on Computer Vision, pp. 206–220 (2002)

  16. Ackermann, J., Fuhrmann, S., Goesele, M.: Geometric point light source calibration. In: Vision Modeling and Visualization, pp. 161–168 (2013)

  17. Camera Calibration Toolbox for Matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/

  18. Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)

    Article  Google Scholar 

  19. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of IEEE International Conference on Computer Vision. IEEE, pp. 839–846 (1998)

  20. Mitra, N.J., Nguyen, A.: Estimating surface normals in noisy point cloud data. In: Proceedings of ACM Symposium on Computational Geometry. ACM, pp. 322–328 (2003)

  21. Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for non-linear least squares problems, 2nd edn. Informatics and Mathematical Modeling, Technical University of Denmark, Lyngby, Denmark, 24C29 (2004)

  22. CHOLMOD. http://www.suitesparse.com

  23. Geomagic Qualify. http://www.geomagic.com

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 61402489).

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Authors and Affiliations

  1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, 410073, Hunan, China

    Liang Xie & Yuhua Xu

  2. School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou, 510275, Guangdong, China

    Xiaohu Zhang

  3. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, Anhui, China

    Wei Bao

  4. DeepInTech, Shenzhen, 518055, Guangdong, China

    Chenpeng Tong

  5. Institute of Digital Media, School of EECS, Peking University, Beijing, 100871, China

    Boxin Shi

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  1. Liang Xie
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  2. Yuhua Xu
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  3. Xiaohu Zhang
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  4. Wei Bao
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  5. Chenpeng Tong
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  6. Boxin Shi
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Correspondence to Yuhua Xu.

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Xie, L., Xu, Y., Zhang, X. et al. A self-calibrated photo-geometric depth camera. Vis Comput 35, 99–108 (2019). https://doi.org/10.1007/s00371-018-1507-9

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