3-D Reconstruction and Optical Measurement of Blade Profiles via Multiview Fine Registration Based on Self-Supervised Simultaneous Optimization

Accurately and robustly aligning multiple viewpoints into a complete profile, i.e., 3-D reconstruction, is crucial for the optical measurement of thin-walled, twisted blade profiles. However, existing mainstream reconstruction methods heavily depend on the motion stability and geometric accuracy of the measurement system. To address this issue, we propose a multiview fine registration framework based on self-supervised simultaneous optimization (MFRSSO) to improve the reconstruction accuracy. Specifically, MFRSSO designs a self-supervised loss to simultaneously score the alignment of all viewpoints and proposes a training-as-optimization paradigm to directly optimize global rigid transformations via gradient descent, avoiding challenges, such as cumulative errors, insufficient robustness, and local optima, commonly found in current point cloud registration methods. In addition, the proposed paradigm eliminates the requirement for parameter generalizability, thereby preventing performance degradation caused by domain differences between training and testing datasets inherent in the traditional train-then-test paradigm of machine learning. Experimental results on three representative blades show that MFRSSO consistently outperforms nine state-of-the-art algorithms in both accuracy and robustness, achieving a reconstruction accuracy of 0.001 mm and improving accuracy by over 70%, demonstrating its significant application prospect.