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Related Experiment Video

Updated: May 20, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

274

Fast underwater scene reconstruction using multi-view stereo and physical imaging.

Shuyi Hu1, Qi Liu1

  • 1School of Future Technology, South China University of Technology, Guangzhou, 511442, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 17, 2025
PubMed
Summary

This study introduces a new method for underwater scene reconstruction, combining Multi-View Stereo (MVS) with a physics-based model. It achieves faster training and rendering while improving depth estimation and image quality.

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Area of Science:

  • Computer Vision
  • Photogrammetry
  • Underwater Imaging

Background:

  • Underwater scene reconstruction is challenging due to light scattering and absorption.
  • Existing Neural Radiance Fields (NeRF) methods are slow for underwater applications.
  • Current methods often require ground-truth depth data, limiting practical use.

Purpose of the Study:

  • To develop a novel, efficient method for high-quality underwater scene reconstruction.
  • To improve depth estimation and rendering accuracy in scattering underwater environments.
  • To eliminate the need for ground-truth depth data, accelerating training and rendering.

Main Methods:

  • Integration of Multi-View Stereo (MVS) with a physics-based underwater image formation model.
  • A two-branch approach: one for depth estimation (MVS cost volume pipeline) and one for physics-based rendering.
Keywords:
3D scene reconstructionMulti-view stereoNovel view synthesisUnderwater scene reconstruction

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  • Utilizing a medium subnet to estimate scattering parameters and a color MLP for rendering.

    • Main Results:

    • Achieved high-quality novel view synthesis in scattering media.
    • Enabled clear view restoration by effectively removing the scattering medium.
    • Demonstrated superior rendering quality and training efficiency compared to existing methods.

    Conclusions:

    • The proposed MVS and physics-based model integration offers a significant advancement in underwater scene reconstruction.
    • The method provides accurate geometric representations and restores true underwater colors.
    • This approach overcomes limitations of previous NeRF-based methods, offering faster and more efficient reconstruction.