Seathru Underwater Imaging Dataset

Key Features:

The five different scenes are selected based on unique underwater imaging features. These include:

• Imaging distance: Variation in the distance between the camera and the scene.
• Camera orientation: Whether the camera is positioned to look forward or downward.
• Optical conditions: Different water clarity levels and lighting conditions to simulate real-world underwater environments.

Each dataset offers multiple overlapping images of the scenes, enabling the construction of 3D models using Structure-from-Motion (SFM) techniques. In our work, we employed Agisoft Metashape Pro to generate depth maps corresponding to the images.

Data Format:

Due to storage constraints, the original RAW files are not included in this release. Instead, we provide linear images in PNG format, scaled down to 30% of their original resolution. If you require access to the full-resolution RAW files, please reach out to us directly.

Depth Map Details:

The depth maps represent distances in meters. Important considerations for depth map interpretation include:

• Zero values: These should be interpreted as NaNs (Not a Number), representing areas where depth information could not be calculated.
• Depth scaling: All depth maps have been scaled in meters, allowing for easy interpretation and integration with other 3D modeling data.

Applications and Use Cases:

The Seathru dataset is particularly useful for a wide range of underwater research and machine learning applications, including:

• 3D Scene Reconstruction: Using SFM, researchers can build accurate 3D models of underwater environments, which are essential for robotic navigation and marine research.
• Color Correction and Image Restoration: The uncorrected images can serve as a baseline for developing algorithms that perform color correction and restoration in challenging underwater conditions.
• Depth Estimation Algorithms: The dataset provides a valuable resource for training machine learning models to improve depth estimation and object detection in underwater environments.