A Flexible Method for Time-of-Flight Camera Calibration Using Random Forest

  • Chi Xu
  • Cheng LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11010)


A learning based approach is proposed to calibrate the geometric distortion of a Time-of-Flight (ToF) camera. Our method is flexible as it requires only a ToF camera and a standard camera calibration chessboard. We treat the noise model of a ToF camera as a black box, and employ random forest to automatically learn the underlying unique noise model. The geometric property of the point-cloud can be effectively restored by the learned distortion model. The method can be used in a range of computer vision applications including e.g. hand pose estimation.


ToF camera Calibration Random forest 


  1. 1.
    Softkinetic (2012).
  2. 2.
    Bradski, G.: The OpenCV library. Doct. Dobbs J. 25(11), 120–126 (2000)Google Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  4. 4.
    Cui, Y., Schuon, S., Chan, D., Thrun, S., Theobalt, C.: 3D shape scanning with a time-of-flight camera. In: CVPR (2010)Google Scholar
  5. 5.
    Denil, M., Matheson, D., de Freitas, N.: Narrowing the gap: random forests in theory and practice. In: ICML (2014)Google Scholar
  6. 6.
    Fanelli, G., Gall, J., Van Gool, L.: Real time head pose estimation with random regression forests. In: CVPR (2011)Google Scholar
  7. 7.
    Fuchs, S., Hirzinger, G.: Extrinsic and depth calibration of ToF-cameras. In: CVPR (2008)Google Scholar
  8. 8.
    Hansard, M., Lee, S., Choi, O., Horaud, R.: Time-of-Flight Cameras: Principles, Methods and Applications. Springer, London (2013). Scholar
  9. 9.
    Hansard, M., Horaud, R., Amat, M., Evangelidis, G.: Automatic detection of calibration grids in time-of-flight images. Comput. Vis. Image Underst. 121, 108–118 (2014)CrossRefGoogle Scholar
  10. 10.
    Herrera, C., Kannala, J., Heikkilä, J., et al.: Joint depth and color camera calibration with distortion correction. IEEE TPAMI 34(10), 2058–2064 (2012)CrossRefGoogle Scholar
  11. 11.
    Jung, J., Lee, J.Y., Jeong, Y., Kweon, I.S.: Time-of-flight sensor calibration for a color and depth camera pair. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1501–1513 (2015)CrossRefGoogle Scholar
  12. 12.
    Kahlmann, T., Remondino, F., Ingensand, H.: Calibration for increased accuracy of the range imaging camera SwissRangerTM. Image Eng. Vis. Metrol. (IEVM) 36(3), 136–141 (2006)Google Scholar
  13. 13.
    Kim, Y.M., Chan, D., Theobalt, C., Thrun, S.: Design and calibration of a multi-view TOF sensor fusion system. In: CVPR Workshops (2008)Google Scholar
  14. 14.
    Lenzen, F., et al.: Denoising strategies for time-of-flight data. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds.) Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. LNCS, vol. 8200, pp. 25–45. Springer, Heidelberg (2013). Scholar
  15. 15.
    Li, S., Xu, C., Xie, M.: A robust O(n) solution to the perspective-n-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1444–1450 (2012)CrossRefGoogle Scholar
  16. 16.
    Lindner, M., Kolb, A.: Calibration of the intensity-related distance error of the PMD ToF-camera. In: Optics East, p. 67640W. International Society for Optics and Photonics (2007)Google Scholar
  17. 17.
    Lindner, M., Schiller, I., Kolb, A., Koch, R.: Time-of-flight sensor calibration for accurate range sensing. Comput. Vis. Image Underst. 114(12), 1318–1328 (2010)CrossRefGoogle Scholar
  18. 18.
    Marco, J., et al.: DeepToF: off-the-shelf real-time correction of multipath interference in time-of-flight imaging. ACM Trans. Graph. 36(6), 1–12 (2017)CrossRefGoogle Scholar
  19. 19.
    Reynolds, M., Dobos, J., Peel, L., Weyrich, T., Brostow, G.: Capturing time-of-flight data with confidence. In: CVPR (2011)Google Scholar
  20. 20.
    Schiller, I., Beder, C., Koch, R.: Calibration of a PMD-camera using a planar calibration pattern together with a multi-camera setup. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 37, 297–302 (2008)Google Scholar
  21. 21.
    Schmidt, M.: Analysis, modeling and dynamic optimization of 3D time-of-flight imaging systems. Ph.D. thesis, Univ. Heidelberg (2011)Google Scholar
  22. 22.
    Schuon, S., Theobalt, C., Davis, J., Thrun, S.: LidarBoost: depth superresolution for ToF 3D shape scanning. In: CVPR (2009)Google Scholar
  23. 23.
    Schweighofer, G., Pinz, A.: Robust pose estimation from a planar target. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2024–2030 (2006)CrossRefGoogle Scholar
  24. 24.
    Su, P.C., Shen, J., Xu, W., Cheung, S.S., Luo, Y.: A fast and robust extrinsic calibration for RGB-D camera networks? Sensors 18(1), 235 (2018)CrossRefGoogle Scholar
  25. 25.
    Tang, D., Chang, H., Tejani, A., Kim, T.K.: Latent regression forest: structured estimation of 3D articulated hand posture. In: CVPR (2014)Google Scholar
  26. 26.
    Wang, L., Gong, M., Zhang, C., Yang, R., Zhang, C., Yang, Y.H.: Automatic real-time video matting using time-of-flight camera and multichannel poisson equations. IJCV 97(1), 104–21 (2012)CrossRefGoogle Scholar
  27. 27.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE TPAMI 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  28. 28.
    Zhu, J., Wang, L., Yang, R., Davis, J.: Fusion of time-of-flight depth and stereo for high accuracy depth maps. In: CVPR (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of AutomationChina University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhanChina
  3. 3.Bioinformatics Institute, A*STARSingaporeSingapore
  4. 4.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada

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