This paper introduces a novel self-supervised learning method for completing depth maps of transparent objects, a challenging task for conventional sensors. By simulating transparent object depth deficits within non-transparent regions, the approach significantly reduces reliance on costly labeled data while achieving comparable performance to supervised methods.