UNDERWATER STEREO MATCHING VIA UNSUPERVISED APPEARANCE AND FEATURE ADAPTATION NETWORKS

Stereo matching has been widely used to estimate depth maps in terrestrial environments. However, it is difficult to achieve appealing performance in underwater environments, since adequate underwater stereo data with groundtruth depth information is not easily available for training an underwater depth estimation model. In addition, the domain gap also leads to the failure of directly applying existing models of terrestrial scenes to underwater scenes. Therefore, this paper proposes a novel underwater depth estimation network which can infer depth maps from real underwater stereo images in an unsupervised adaptation manner. The proposed learning pipeline contains style adaptation (SA) in appearance space and feature adaptation (FA) in semantic space to progressively adapt the depth estimation models to underwater domain. Experimental results show that by integrating the proposed adaptation modules into the off-the-shelf stereo matching backbones, our method achieves a superior performance of underwater depth estimation compared to other state-of-the-art methods.