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

Junhong Zhao¹, Bing Xue¹, Ross Vennell²

¹Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand.

Journal of the Royal Society of New Zealand

|April 16, 2026

Summary

This summary is machine-generated.

This study introduces neural radiance fields (NeRF) for 3D mussel farm reconstruction from video. This advanced technique improves monitoring by capturing fine details and enabling better analysis of farm dynamics.

Keywords:

3D reconstruction aquaculture large‐scale scene reconstruction mussel farm intelligence neural radiance field (NeRF)

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Area of Science:

Marine Engineering
Computer Vision
Photogrammetry

Background:

Mussel farming requires advanced monitoring, but traditional methods are limited.
Existing 3D reconstruction techniques struggle with details and water reflections.

Purpose of the Study:

To apply neural radiance fields (NeRF) for detailed 3D reconstruction of mussel farms.
To develop a multi-NeRF framework for enhanced detail capture and water surface modeling.
To improve intelligent monitoring and decision-making in aquaculture.

Main Methods:

Utilized vessel-captured video footage for 3D scene reconstruction.
Developed a multi-NeRF framework with region-specific modeling.
Implemented a geometry regularization method for water surface reconstruction.

Main Results:

Achieved superior 3D reconstruction quality compared to previous methods.
Demonstrated improved mesh completeness and detail handling (specular/diffuse textures).
Enabled realistic novel view synthesis for comprehensive farm analysis.

Conclusions:

NeRF technology offers significant advancements for mussel farm 3D reconstruction.
The proposed methods enhance the understanding of farm geometry and dynamics.
This contributes to intelligent monitoring and informed decision-making in the aquaculture industry.