Distance Overestimation Error Correction Method (DOEC) of Time of Flight Camera Based on Pinhole Model

  • Le Wang
  • Minrui Fei
  • Hakuan WangEmail author
  • Zexue Ji
  • Aolei Yang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 924)


Depth cameras with Time of Flight (ToF) technology are widely used in machine vision and various measurement tasks. However, due to hardware conditions and imaging characteristics, multiple errors limit the further application of the ToF camera. This paper classified errors into errors caused by non-imaging principle and error caused by imaging principle. In order to simplify the experimental procedure and improve the efficiency of errors correction, a simple and feasible method is used to correct errors caused by non-imaging principle, and an evaluation function is proposed to determine the optimal reference distance, so as to select appropriate integration time and global offsets. To tackle the radial distance error, Distance Overestimation Error Correction method (DOEC) based on the principle of pinhole imaging is proposed to further improve the accuracy of depth data. Finally, error correction methods proposed in this paper are verified by experiments, and the segmentation of different depth ranges is successfully achieved by using the modified data, prove the effectiveness of the proposed methods.


Time of flight Error correction Pinhole imaging Plan segmentation Data accuracy 



This work is supported by National Science Foundation of China (61473182, 61773253), Science and Technology Commission of Shanghai Municipality (15JC1401900), Natural Science Foundation of Shanghai (No. 18ZR1415100).


  1. 1.
    Pertile, M., Chiodini, S., Giubilato, R., Debei, S.: Calibration of extrinsic parameters of a hybrid vision system for navigation comprising a very low resolution time-of-flight camera. In: IEEE International Workshop on Metrology for Aerospace, pp. 391–396 (2017)Google Scholar
  2. 2.
    Lin, J., Liu, Y., Suo, J., Dai, Q.: Frequency-domain transient imaging. IEEE Trans. Pattern Anal. Mach. Intell. 39, 937–950 (2016)CrossRefGoogle Scholar
  3. 3.
    Adam, A., Dann, C., Yair, O., Mazor, S., Nowozin, S.: Bayesian time-of-flight for realtime shape, illumination and albedo. IEEE Trans. Pattern Anal. Mach. Intell. 39, 851–864 (2017)CrossRefGoogle Scholar
  4. 4.
    Anwer, A., Ali, S.S.A., Khan, A., Mériaudeau, F.: Underwater 3D scene reconstruction using Kinect v2 based on physical models for refraction and time of flight correction. IEEE Access 5, 1–11 (2017)CrossRefGoogle Scholar
  5. 5.
    Shim, H., Lee, S.: Recovering translucent objects using a single time-of-flight depth camera. IEEE Trans. Circuits Syst. Video Technol. 26, 841–854 (2016)CrossRefGoogle Scholar
  6. 6.
    Francis, S.L.X., Anavatti, S.G., Garratt, M., Shim, H.: A ToF-camera as a 3D vision sensor for autonomous mobile robotics. Int. J. Adv. Robot. Syst. 12, 1 (2015)CrossRefGoogle Scholar
  7. 7.
    Illadequinteiro, J., Brea, V.M., López, P., Cabello, D., Doménechasensi, G.: Distance measurement error in time-of-flight sensors due to shot noise. Sensors 15, 24–42 (2015)Google Scholar
  8. 8.
    Fürsattel, P., et al.: A comparative error analysis of current time-of-flight sensors. IEEE Trans. Comput. Imaging 2, 27–41 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Belhedi, A., Bartoli, A., Bourgeois, S., Gay-Bellile, V.: Noise modelling in time-of-flight sensors with application to depth noise removal and uncertainty estimation in three-dimensional measurement. Comput. Vis. IET 9, 967–977 (2015)CrossRefGoogle Scholar
  10. 10.
    Ghorpade, V.K., Checchin, P., Trassoudaine, L.: Line-of-sight-based ToF camera’s range image filtering for precise 3D scene reconstruction. In: European Conference on Mobile Robots, pp. 1–6 (2015)Google Scholar
  11. 11.
    Hertzberg, C., Frese, U.: Detailed modeling and calibration of a time-of-flight camera. In: International Conference on Informatics in Control, Automation and Robotics, pp. 568–579 (2015)Google Scholar
  12. 12.
    Konno, Y., et al.: Accurate plane estimation based on the error model of time-of-flight camera. In: Second IEEE International Conference on Robotic Computing, pp. 304–307 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Le Wang
    • 1
  • Minrui Fei
    • 1
  • Hakuan Wang
    • 1
    Email author
  • Zexue Ji
    • 1
  • Aolei Yang
    • 1
  1. 1.Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and AutomationShanghai UniversityShanghaiChina

Personalised recommendations