  This paper proposes a photorealistic real-time dense 3D mapping system that
utilizes a learning-based image enhancement method and mesh-based map
representation. Due to the characteristics of the underwater environment, where
problems such as hazing and low contrast occur, it is hard to apply
conventional simultaneous localization and mapping (SLAM) methods. Furthermore,
for sensitive tasks like inspecting cracks, photorealistic mapping is very
important. However, the behavior of Autonomous Underwater Vehicle (AUV) is
computationally constrained. In this paper, we utilize a neural network-based
image enhancement method to improve pose estimation and mapping quality and
apply a sliding window-based mesh expansion method to enable lightweight, fast,
and photorealistic mapping. To validate our results, we utilize real-world and
indoor synthetic datasets. We performed qualitative validation with the
real-world dataset and quantitative validation by modeling images from the
indoor synthetic dataset as underwater scenes.
