A Hybrid-Level Weighted Multi-View Measurement Method for Industrial Geometric Features

Multi-view measurement of industrial geometric features is susceptible to reduced three-dimensional (3D) reconstruction accuracy due to inconsistent imaging quality, local edge localization errors, and unfavorable observation poses. To address these issues, this paper proposes a hybrid-level weighted multi-view measurement method that evaluates the reliability of cross-view observations from two complementary aspects: local edge quality and global observation geometry. At the local level, a subpixel grayscale model is established along the edge-normal direction to derive a local quality weight. At the global level, an observation-geometry weight is defined based on the angle between the feature-plane normal and the camera optical axis. The two weights are then hierarchically normalized and multiplicatively fused for each set of cross-view correspondences. The resulting fused weights are incorporated into multi-view 3D reconstruction, thereby reducing the propagation of errors arising from low-quality local observations and unfavorable viewpoints to the reconstruction. Experiments show that, compared with equal-weight reconstruction, the proposed method reduces the mean error in center-to-center distance measurement by 41.5%, and the mean errors in square-hole width and height measurement by 22.8% and 25.8%, respectively. These results demonstrate the high precision, improved stability, and strong extensibility of the proposed method for industrial planar geometric feature measurement.