Seathru Underwater Imaging Dataset
Authors/Creators
Description
Description:
The Seathru Underwater Imaging Dataset comprises five distinct datasets (D1-D5), each designed to address different aspects of underwater imaging. Each dataset contains:
- A set of N linear images captured without any color correction.
- N depth maps corresponding to each linear image.
The exact number of images (N) varies between datasets. For a detailed breakdown of each dataset’s size and specifications, please refer to the accompanying research paper.
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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.
This dataset is sourced from Kaggle.