Accuracy Evaluation of Locally Deformed Angle Steel Point Cloud Models Based on Spatial Euclidean Distance

Local deformation is a common damage to steel structures， and 3D reconstruction is an important approach to evaluate the bearing capacity of locally deformed angle steels. To accurately evaluate the accuracy of the SfM （Structure from Motion）-MVS （Multi-View Stereo） 3D reconstruction algorithm in establishing point cloud models of locally deformed steel components， this study proposed an accuracy evaluation method for angle steel point cloud models based on spatial Euclidean distance. Four types of locally deformed angle steels with two thicknesses were selected as the research objects， and an accuracy verification test was carried out on the digital image models of locally deformed steel components. The iterative closest point algorithm was used to align the test point cloud with the reference point cloud in space， and the accuracy of the point cloud model was quantified according to the Euclidean distance between corresponding points， so as to verify the accuracy of the angle steel point cloud model from a 3D perspective. The results showed that through the accuracy evaluation of the angle steel point cloud model， in the height and width directions， the points within the allowable range of dimensional deviation in the 3D point cloud models of the four types of locally deformed angle steels accounted for approximately 98% of the total number of point clouds in the models； in the thickness direction， the average error of the 5-mm-thick angle steel point cloud model was approximately 1.09 mm， while the average error of the 10-mm-thick angle steel point cloud model was approximately 1.02 mm； in the local deformation area， the average error of the four angle steel point cloud models was approximately 1.00 mm.