Research on Interval Constraint Range Domain Algorithm for 3D Reconstruction Algorithm Based on Binocular Stereo Vision

  • Caiqing Wang
  • Shubin WangEmail author
  • Enshuo Zhang
  • Jingtao Du
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


For 3D reconstruction algorithm based on binocular stereo vision only approximately restores the 3D shape of the object, but it does not analyze the performance of using 3D reconstruction algorithm based on the model of binocular stereo vision measuring distance device to restore object, an interval constraint range domain algorithm is proposed in this paper. In this paper, this algorithm finds the boundary points of the object, the performance of using 3D reconstruction algorithm to restore object depends on the proportion of the number of points that fall within the constraint interval to the total number of points. The interval constraint range domain algorithm analyzes the performance of the 3D reconstruction algorithm to recover the object, that is, analyzes the measurement accuracy of the algorithm. Under the condition of the resolution of the image sensor used in the algorithm is identical, the simulation results show that the accuracy of the algorithm is determined by the spacing of the image sensor, the position, and shape of the constraint interval.


Binocular stereo vision 3D reconstruction Interval constraint domain algorithm 



Shubin Wang ( is the correspondent author and this work was supported by the National Natural Science Foundation of China (61761034, 61261020), and the Natural Science Foundation of Inner Mongolia, China (2016MS0616), and the Enhancing Comprehensive Strength Foundation of Inner Mongolia University (No. 10000-16010109-57).


  1. 1.
    Xu G, Li X, Su J, Pan H, Tian G. Precision evaluation of three-dimensional feature points measurement by binocular vision. J Opt Soc Korea. 2011;15(1):30–7.CrossRefGoogle Scholar
  2. 2.
    Wang W, Li HL. 3D reconstruction based on digital camera images. J Univ Shanghai Sci Technol. 2005;27(5):429–32.Google Scholar
  3. 3.
    Gao LF, Gai YX, Fu S. Simultaneous localization and mapping for autonomous mobile robots using binocular stereo vision system. In: 2007 IEEE international conference on mechatronics and automation; 2007. p. 326–30.Google Scholar
  4. 4.
    Zhu K. Real-time tracking and measuring of moving object based on binocular vision. Beijing: Beijing Jiaotong University; 2008. p. 1–103.Google Scholar
  5. 5.
    Jia T, Tu M, Jiang Y, Zhang S. 3D temperature distribution model based on vision method. In: 2016 IEEE international conference on robotics and biomimetics (ROBIO); 2016. p. 852–5.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Caiqing Wang
    • 1
  • Shubin Wang
    • 1
    Email author
  • Enshuo Zhang
    • 1
  • Jingtao Du
    • 1
  1. 1.College of Electronic Information Engineering, Inner Mongolia UniversityHohhotChina

Personalised recommendations