Neural Radiance Field-Based 3D Reconstruction and View Synthesis for Mussel Farm Environments

As the mussel farming industry grows, the demand for advanced monitoring and management solutions intensifies. Traditional methods rely heavily on on-site observations and frequent boat trips to monitor buoy flotation and other operational elements, often resulting in limited and sporadic assessments that can hinder the decision-making process. This article proposes using 3D reconstruction techniques to reconstruct mussel farm scenes from the vessel-captured video footage, allowing for holistic visualizations from various perspectives and enabling comprehensive post analysis of the mussel farm dynamics. While previous efforts relied on traditional Structure from Motion and multiview techniques for mussel farm scene reconstruction, they often struggled to capture fine details and faced challenges with reflective water surfaces due to their reliance on local features. In this work, we are the first to explore and extend the capabilities of neural radiance field (NeRF) for mussel farm reconstruction. To overcome the practical challenges of this unique environment, we propose a multi-NeRF framework with region-specific modeling, enabling the capture of both the global scene and finer details of key elements such as buoys. Furthermore, we introduce a geometry regularization method to improve the planar reconstruction of the water surface. Our results demonstrate significant advancements in 3D reconstruction quality over previous methods, particularly in mesh completeness and the precise handling of specular and diffuse texture details while also enabling realistic novel view synthesis. These advancements, designed particularly for mussel farm applications, can contribute to its intelligent monitoring and management by providing a comprehensive understanding of the farm's geometry and dynamics, ultimately facilitating more informed decision-making processes.