Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison

Abstract

Corals serve as the foundational habitat-building organisms within reef ecosystems, constructing extensive structures that extend over vast distances. However, their inherent fragility and vulnerability to various threats render them susceptible to significant damage and destruction. The application of advanced 3D reconstruction technologies for high-quality modeling is crucial for preserving them. These technologies help scientists to accurately document and monitor the state of coral reefs, including their structure, species distribution and changes over time. Photogrammetry-based approaches stand out among existing solutions, especially with recent advancements in underwater videography, photogrammetric computer vision, and machine learning. Despite continuous progress in image-based 3D reconstruction techniques, there remains a lack of systematic reviews and comprehensive evaluations of cutting-edge solutions specifically applied to underwater coral reef images. The emerging advanced methods may have difficulty coping with underwater imaging environments, complex coral structures, and computational resource constraints. They need to be reviewed and evaluated to bridge the gap between many cutting-edge technical studies and practical applications. This paper focuses on the two critical stages of these approaches: camera pose estimation and dense surface reconstruction. We systematically review and summarize classical and emerging methods, conducting comprehensive evaluations through real-world and simulated datasets. Based on our findings, we offer reference recommendations and discuss the development potential and challenges of existing approaches in depth. This work equips scientists and managers with a technical foundation and practical guidance for processing underwater coral reef images for 3D reconstruction....

References in corpus (14)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
• 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
• Nerfstudio: A Modular Framework for Neural Radiance Field Development
• SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
• SfM-Net: Learning of Structure and Motion from Video
• DISK: Learning local features with policy gradient
• BA-Net: Dense Bundle Adjustment Network
• MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth
• NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance
• Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes
• MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo
• Visual Geometry Grounded Deep Structure From Motion
• Dens3R: A Foundation Model for 3D Geometry Prediction