• Infrared and Laser Engineering
  • Vol. 49, Issue 7, 20190492 (2020)
Yujing Qiao, Baoming Jia, Jingang Jiang, and Jingyi Wang
Author Affiliations
  • 哈尔滨理工大学 机械动力工程学院,黑龙江 哈尔滨 150000
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    DOI: 10.3788/IRLA20190492 Cite this Article
    Yujing Qiao, Baoming Jia, Jingang Jiang, Jingyi Wang. Networking method of multi-view stereo-vision measurement network[J]. Infrared and Laser Engineering, 2020, 49(7): 20190492 Copy Citation Text show less
    Visual measurement network model
    Fig. 1. Visual measurement network model
    Camera position layout diagram
    Fig. 2. Camera position layout diagram
    Schematic diagram of the normal vector of the triangle
    Fig. 3. Schematic diagram of the normal vector of the triangle
    Viewpoint simulation result distribution diagram
    Fig. 4. Viewpoint simulation result distribution diagram
    Experimental platform and real shot
    Fig. 5. Experimental platform and real shot
    Shade point cloud and ellipsoid initial measurement network
    Fig. 6. Shade point cloud and ellipsoid initial measurement network
    Image of three viewpoint positions
    Fig. 7. Image of three viewpoint positions
    Measurement network for aperture f/1.4,f/2.0,f/2.8,f/4.0
    Fig. 8. Measurement network for aperture f/1.4,f/2.0,f/2.8,f/4.0
    Coverage comparison chart
    Fig. 9. Coverage comparison chart
    Three-dimensional reconstruction of the lampshade
    Fig. 10. Three-dimensional reconstruction of the lampshade
    Constraint formulaDescription
    :Number of images taken per viewpoint (take a picture of one of the viewpoints in this article) ; :Specified precision quantity; :Image error; :Design factor (0.4-0.7),(set to 0.6 in this article) ; :Maximum length of object to be measured
    :Size of object to be measured (the minimum distance between two points on the object to be measured is designed by the operator in this article); :Image size of the object to be measured (the minimum distance between two points in the image is 1 pixel in this article); :pixel :Angle between the optical axis and the object to be measured (that is, the table point of the object to be measured),the parameter is set to 90 °
    :Half the angle of view ; :Minimum number of frames in an image; :Focal length
    Table 1. Constraint formula and description
    Image\begin{document}${\rm{Camera } }\;{c_1}$\end{document}\begin{document}${\rm{Camera } }\;{c_2}$\end{document}\begin{document}${\rm{Camera } }\;{c_3}$\end{document}\begin{document}${\rm{Camera } }\;{c_4}$\end{document}
    Table 2. Camera ${c_1}$, ${c_2}$, ${c_3}$, ${c_4}$calibration results
    Aperturef/1.4 f/2 f/2.8 f/4 f/11 f/16
    Spherical network844230303030
    Ellipsoidal network683022222222
    Quantity difference16128888
    Efficiency improvement rate19%28%26.7%26.7%26.7%26.7%
    Table 3. Camera number comparison
    Aperturef/1.4 f/2.0 f/2.8 f/4.0 f/11 f/16
    Spherical network1.389 11.352 01.358 41.309 81.328 61.330 4
    Ellipsoidal network1.115 61.102 31.136 51.111 01.153 01.154 1
    Accuracy difference0.273 50.249 70.221 90.198 80.175 60.176 3
    Accuracy improvement rate20%18.5%16.3%15.2%13.2%13.3%
    Table 4. Comparative analysis of precision data
    Yujing Qiao, Baoming Jia, Jingang Jiang, Jingyi Wang. Networking method of multi-view stereo-vision measurement network[J]. Infrared and Laser Engineering, 2020, 49(7): 20190492
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