Underwater Image Rendering aims to generate a true-tolife underwater image from a given clean one, which could be applied to various practical applications such as underwater image enhancement, camera filter, and virtual gaming. We explore two less-touched but challenging problems in underwater image rendering, namely, i) how to render diverse underwater scenes by a single neural network? ii) how to adaptively learn the underwater light fields from natural exemplars, i,e., realistic underwater images? To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering). Specifically, UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models. Compared with existing underwater image generation methods, UWNR utilizes the natural light field to simulate the main characteristics ofthe underwater scene. Thus, it is able to synthesize a wide variety ofunderwater images from one clean image with various realistic underwater images. Extensive experiments demonstrate that our approach achieves better visual effects and quantitative metrics over previous methods. Moreover, we adopt UWNR to build an open Large Neural Rendering Underwater Dataset containing various types of water quality, dubbed LNRUD. The source code and LNRUD are available at https: //github.com/Ephemeral182/UWNR.
The Ohio State University（俄亥俄州立大学）；Chongqing Changan Automobile Co Ltd（重庆长安汽车股份有限公司）
Driving simulators play a large role in developing and testing new intelligent vehicle systems. The visual fidelity of the simulation is critical for building vision-based algorithms and conducting human driver experiments. Low visual fidelity breaks immersion for human-in-the-loop driving experiments. Conventional computer graphics pipelines use detailed 3D models, meshes, textures, and rendering engines to generate 2D images from 3D scenes. These processes are labor-intensive, and they do not generate photorealistic imagery. Here we introduce a hybrid generative neural graphics pipeline for …
Underwater360: Reconstructing Underwater Scenes from Panoramic Images with Omnidirectional Gaussian Splatting
School of Software, Dalian University of Technology（大连理工大学软件学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian
Huazhong University of Science and Technology（华中科技大学）；Zhongguancun Academy（中关村学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Zhengzhou University（郑州大学）；Hainan University（海南大学）；Shenzhen University（深圳大学）
AquaGS: Fast Underwater Scene Reconstruction with SfM-Free Gaussian Splatting
School of Electronic Information and Electrical Engineering（电子信息与电气工程学院）；Shanghai Jiao Tong University（上海交通大学）；State Key Laboratory of Ocean Engineering（海洋工程国家重点实验室）
GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔亥姆霍兹海洋研究中心）
Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development …
3D-USE: From Image-Level to Scene-Level Underwater Enhancement
College of Computer Science, Nankai University（南开大学计算机学院）；NKIARI（南开大学人工智能研究院（深圳））；School of Automation, Southeast University（东南大学自动化学院）；Advanced Ocean Institute of Southeast University, Nantong（东南大学南通先进海洋研究院）
FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation
State Key Laboratory of Virtual Reality Technology and Systems, SCSE&QRI, Beihang University（虚拟现实技术与系统国家重点实验室，北京航空航天大学）；Geely Automobile Research Institute (Ningbo) Co., Ltd（吉利汽车研究院（宁波）有限公司）
University of Florida（佛罗里达大学）；Cornell University（康奈尔大学）；Naval Surface Warfare Center, Panama City Division（巴拿马城分部海军水面作战中心）；Naval Undersea Warfare Center, Newport（纽波特海军水下作战中心）
We propose a novel method to generate underwater object imagery that is acoustically compliant with that generated by side-scan sonar using the Unreal Engine. We describe the process to develop, tune, and generate imagery to provide representative images for use in training automated target recognition (ATR) and machine learning algorithms. The methods provide visual approximations for acoustic effects such as back-scatter noise and acoustic shadow, while allowing fast rendering with C++ actor in UE for maximizing the size of potential ATR training datasets. Additionally, we provide analysis o…
I-X and Department of Electrical and Electronic Engineering, Imperial College London, UK（I-X 和 电气与电子工程系，帝国理工学院伦敦分校）；Frontier Robotics, The National Robotarium, Edinburgh UK（前沿机器人技术，国家机器人中心，爱丁堡）
