MyUnderwaterWorld: Intelligent Underwater Scene Representation

人们对海洋的探索已经有数百年的历史，这些活动仍在继续，但它们总是受到潜水专家数量、技术，特别是成本的限制。先进的成像技术可以将水下发现转移给具有特定知识的陆上专家，如地质学家，考古学家和生物学家。这些图像序列的三维（3D）重建增强了对水下生物，物体和海底的了解。然而，目前的解决方案尚未提供水下3D表示的高分辨率和清晰度，而无需数月的强烈增强和处理时间。这主要是因为数据和计算复杂性的限制，因为很明显，由于失真、光的后向散射和浊度条件，处理水下环境的序列是具有挑战性的。MyUnderwaterWorld项目旨在为基于人工智能（AI）的开发提供水下图像的深入分析，从而为图像质量增强和水下场景的高分辨率3D场景表示提供新的框架，其中包含海底和感兴趣的对象。我们假设，三维场景可以准确地建模，直接从原始的水下数据使用定义良好的先验知识。这可以通过描述不同和可靠的水下数据集来实现。我们将联合收割机实时视觉SLAM和稀疏辐射场分层，训练与一个新的损失函数开发的先验知识的水下。这将提高3D表示的质量，并提供更高效和灵活的工作流程。它还将促进更稳健的特征提取，以用于随后的基于机器的处理，并促进更有效的压缩以用于交付。

The oceans have been explored for hundreds of years and the activities still continue, but they are always limited by the number of diving experts, technologies and in particular costs. Advanced imaging enables transferable underwater discovery to onshore experts with specific knowledge required, such as geologists, archaeologists and biologists. Three-dimensional (3D) reconstruction from these image sequences enhance understanding of underwater organisms, objects and the seabed. However, current solutions have not yet provided high resolution and definition of underwater 3D representation without months of intense enhancements and processing time. This is mainly because of limitation of data and computational complexity as, obviously, processing the sequences of underwater environments is challenging due to distortion, backscatter of light and turbidity conditions. MyUnderwaterWorld project aims to provide intensive analysis of underwater imagery for Artificial Intelligence (AI)-based development, leading to a novel framework for image quality enhancement and high-resolution 3D scene representation of underwater scene, which contains seabed and objects of interest. We hypothesise that the 3D scene could be modelled accurately and directly from raw underwater data using well-defined prior knowledge. This could be achieved by characterising diverse and reliable underwater datasets. We will combine real-time visual SLAM and sparse radiance fields hierarchically, trained with a novel loss function developed from prior knowledge of underwater. This will improve quality of 3D representation, and offer more efficient and flexible workflows. It will also facilitate more robust feature extraction for subsequent machine-based processing and more efficient compression for delivery.