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Unmanned aerial image dataset: Ready for 3D reconstruction
Mozhdeh Shahbazi1, Patrick Ménard2, Gunho Sohn3
1Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada.
Data in Brief
|June 14, 2019
Summary
This study introduces a new dataset for evaluating 3D reconstruction from unmanned aerial vehicle (UAV) imagery. The data supports testing photogrammetric and structure-from-motion workflows for accurate geospatial modeling.
Area of Science:
- Geomatics Engineering
- Photogrammetry
- Computer Vision
Background:
- Unmanned aerial vehicles (UAVs) are increasingly used for geospatial data collection.
- Advancements in imaging and computing enable sophisticated 3D modeling from UAV data.
- Accurate 3D models are crucial for applications like structural health monitoring.
Purpose of the Study:
- To present a comprehensive dataset for evaluating 3D reconstruction techniques using UAV imagery.
- To facilitate the assessment of photogrammetric and structure-from-motion workflows.
- To provide reliable ground-truth data for validating 3D models.
Main Methods:
- Collected 158 high-resolution UAV images with >80% endlap.
- Acquired 3D coordinates for 109 ground control and check points.
- Generated 2D correspondence points (>40K) and a dense point cloud via terrestrial laser scanning.
Main Results:
- The dataset comprises high-quality aerial imagery suitable for 3D reconstruction.
- Includes precise ground truth data (GCPs, dense point cloud) for validation.
- Offers multi-view stereo images for detailed scene analysis.
Conclusions:
- The presented dataset is valuable for benchmarking and developing UAV-based 3D reconstruction methods.
- Enables rigorous evaluation of photogrammetric and structure-from-motion algorithms.
- Supports advancements in accurate geospatial modeling for critical applications.
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