Semi-supervised generative adversarial network to estimate the depth map of underwater targets in acoustic images for 3D reconstruction

Owing to the unique imaging formulation principle of acoustic cameras, certain dimensions of the target may be lost in acoustic images. Some studies have been conducted to retrieve these missing details from a single pseudo front depth that contains 3D information about the target. Nevertheless, the use of a single pseudo front depth may face ambiguity problems in recovering the 3D information of acoustic targets. Therefore, this paper presents a novel semi-supervised generative adversarial network (SGAN) for estimating the depth of two adjacent acoustic images at know relative sonar poses, which generates high-accuracy reconstruction results, while reducing the dependence on the labeled ground truth of acoustic targets. Specifically, we design a semi-supervised depth estimation framework that utilizes image warping for semi-supervised training, resulting in denser depth maps. Various datasets containing underwater targets and terrain were generated to simulate real underwater scenarios. Experiments with simulator data and real images verify the feasibility of using SGAN for depth estimation in 3D target reconstruction.