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

   Underwater visual systems greatly suffer from blurry textures and low color contrast due to the inevitable light propagation. These issues can significantly degrade the perception for stable robotic operations. 

  In this paper, we introduce a novel learning-based sensing system that tackles the multidimensional vision tasks underwater; concretely, we deal with image enhancement, depth estimation, and uncertainty for 3D visual systems. Also, we propose a TRIDENT model that takes three objectives with fast and light weight; TRIDENT consists of parallelized three decoders and one backbone structure for efficient feature sharing. In addition, it is designed to be trained on a three-stage hierarchical feature space 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. It also has better efficiency than the others for both memory size and reference time, despite performing three tasks. Finally, we prove that our proposed joint learning is robust to feature matching and has no limitations in extending 2D to 3D vision tasks.