MyUnderwaterWorld: Intelligent Underwater Scene Representation

MyUnderwaterWorld：智能水下场景表示

• 批准号: EP/Y002490/1
• 负责人: Nantheera Anantrasirichai
• 金额: $ 20.98万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY002490%2F1
• 关键词: MyUnderwaterWorld; Intelligent; Underwater; Scene; Representation

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.

海洋探索已有数百年历史，活动仍在继续，但始终受到潜水专家数量、技术尤其是成本的限制。先进的成像技术可以将水下发现转移给具有所需特定知识的陆上专家，例如地质学家、考古学家和生物学家。根据这些图像序列进行三维 (3D) 重建可增强对水下生物、物体和海底的了解。然而，如果没有数月的强烈增强和处理时间，当前的解决方案还无法提供水下 3D 表示的高分辨率和清晰度。这主要是因为数据和计算复杂性的限制，显然，由于失真、光的反向散射和浑浊条件，处理水下环境的序列具有挑战性。 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，从而形成一种新颖的框架，用于图像质量增强和水下场景的高分辨率 3D 场景表示，其中包含海底和感兴趣的物体。我们假设可以使用明确定义的先验知识，根据原始水下数据直接准确地对 3D 场景进行建模。这可以通过表征多样化且可靠的水下数据集来实现。我们将分层地结合实时视觉 SLAM 和稀疏辐射场，并使用根据水下先验知识开发的新颖损失函数进行训练。这将提高 3D 表示的质量，并提供更高效、更灵活的工作流程。它还将有助于为后续基于机器的处理提供更强大的特征提取，并为交付提供更高效的压缩。