Abstract: Stereo-vision network measurement was the core technology for 3D measurement. In order to ensure the full coverage and reconstruction accuracy of 3D reconstruction, the camera needed to be intensively shot during the measurement process, resulting in slow 3D measurement and large calculation.Therefore, a multi-view stereo-vision measurement network networking method was proposed to solve the above problems. Firstly, the object model was obtained by SFM technology (motion recovery structure), establishing the ellipsoid reference coordinates, estimating the optimal distance between the camera and the object to be tested, and arranging the initial viewpoint position. Secondly, the minimum number of cameras, to achieve full coverage 3D imaging, was filtered based on visual constraints to cluster analysis and loop iteration of the initial viewpoint. Finally, the measurement experiment was carried out, and the ellipsoidal measurement network with the lampshade as the object to be tested was arranged. The comparison of the number of cameras, coverage and measurement accuracy with the spherical measurement network was carried out at different depths of field. The experimental results show that 22 viewpoints are selected through the final iteration of the method, so that the coverage rate reached 100%. The standard deviation of the measurement accuracy was stable to 1.1 mm and the measurement efficiency was significantly improved compared with the spherical network. The original appearance of the object to be tested was restored through the 3D reconstruction of the lampshade rendering, which verified the feasibility of the proposed method.