TRIDENT: Efficient Triple-Task Learning of Dehazing, Depth, and Uncertainty Estimation for Underwater 3-D Robot Visual Perception

Abstract

Underwater visual systems often suffer from blurry textures and low color contrast due to the inevitable light propagation. These issues can significantly degrade the perception of stable robotic operations. In this article, we introduce a novel learning-based sensing system that tackles the multidimensional vision tasks in underwater; concretely, we deal with image enhancement, depth estimation, and uncertainty for 3-D visual systems. Also, we propose a TRIDENT model in a fast and lightweight manner; TRIDENT consists of three parallelized decoders and one backbone structure for efficient feature sharing. In addition, it is designed to be trained to express complex parameterization. In experimental evaluation on several standard datasets, we demonstrate that TRIDENT significantly outperforms other existing methods on image enhancement and depth estimation. Despite performing three tasks, our model has better efficiency than the others for both memory size and inference time. Finally, our joint learning approach demonstrates robustness in feature matching and seamlessly extends from 2-D to 3-D vision tasks. Supplements are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/underwater-trident/home</uri>.