Deployment of a deep-learning based multi-view stereo approach for measurement of ship shell plates

Abstract

In this work, a measurement system integrated with a deep-learning based multi-view stereo (MVS) approach is developed to measure ship shell plates. Specifically, a deep learning architecture of CasMVSNet is deployed for depth map inference from multi-view images, which can remarkably decrease the dense reconstruction time and GPU memory consumption. This is the first report that a learned MVS architecture is deployed to three-dimensional (3D) reconstruction of ship shell plates. Measurement experiments are then performed for three typical hull plates to evaluate the accuracy, efficiency and completeness of the proposed measurement method. The results suggest that the complete point cloud data of the curved hull plates can be reconstructed in about 3 min with the average of errors less than 1 mm, which fulfills the requirements of precision and efficiency in shipbuilding production. Compared with traditional wooden templates, the proposed measurement method is more accurate, efficient and inexpensive. The developed measurement system with quantitative data can also be readily integrated with the 3D computer numerical control (CNC) plate bending machine. Moreover, the robustness and flexibility of the proposed measurement method have been verified by comparison with the measurement method based on active binocular stereovision.