A vision measurement method for ship hull plates based on multi-view stereo and image segmentation

Abstract

The background denoising of point clouds is challenging for the measurement of ship hull plates. To address this issue, a vision measurement method is proposed by combining an identity cost volume network (ICV-Net) and a segment anything model (SAM). By applying the ship hull plate masks segmented by SAM to the corresponding depth maps inferred by ICV-Net, the background noise can be directly removed at the depth map level. Additionally, an edge extraction optimization algorithm complementary to SAM is proposed to eliminate the thickness-related noise. Moreover, an efficient depth map refinement algorithm is proposed by imposing the smoothness prior of ship hull plate surfaces and multi-view photometric consistency. Sub-pixel accurate depth maps of ship hull plates can be obtained by the depth map refinement. Experimental results suggest that the proposed method can automatically, accurately and efficiently remove the background noise of ship hull plates, which is crucial toward practical application.