Fast Monocular Depth Estimation for Underwater Robotics Leveraging Attenuation Differences As Supplementary Information

Abstract

Underwater and in-air environments exhibit distinct imaging characteristics, which should be carefully considered and effectively exploited for accurate depth estimation. In this work, we analyze the effectiveness of wavelength-dependent attenuation for underwater depth estimation and show that it is helpful but insufficient to perform depth estimation independently. Therefore, we propose a fast underwater monocular depth estimation network that incorporates underwater light absorption difference (ULAD) as supplementary information. Compared with methods that rely solely on RGB input, the proposed approach provides more accurate depth predictions. In our network, RGB and ULAD features are extracted by MobileNetV4 and fused using FusionMamba, followed by decoding and refinement with a micro Vision Transformer. The network is trained on the USOD10K dataset and evaluated on both its test set and the FLSea dataset. Experimental results demonstrate that our method achieves more accurate depth estimation and higher efficiency compared with other lightweight networks. Furthermore, Compared with existing state-of-the-art fast underwater depth estimation methods, our network further reduces the number of parameters by 10% and improves inference speed by 43%.