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SynthOutdoor: A synthetic dataset for 3D outdoor light estimation
Marcello Zanardelli1, Mahyar G Moghaddam1, Riccardo Leonardi1
1Department of Information Engineering, CNIT - University of Brescia, Via Branze, 38, 25133 Brescia, Italy.
Data in Brief
|July 29, 2024
Summary
Researchers introduce SynthOutdoor, a new dataset for 3D light direction estimation. This dataset aids in computer vision tasks by providing diverse lighting and detailed scene information.
Area of Science:
- Computer Vision
- Computer Graphics
- Machine Learning
Background:
- Accurate 3D light direction estimation is crucial for realistic rendering and scene understanding.
- Existing datasets often lack diversity in lighting conditions or comprehensive ground truth for distant lighting scenarios.
Purpose of the Study:
- To introduce SynthOutdoor, a novel, large-scale synthetic dataset for 3D light direction estimation.
- To address the data scarcity in estimating distant light sources and provide rich ground truth information.
Main Methods:
- Generated a dataset of 39,086 high-resolution images using a Unity3D-based software.
- Simulated a solar cycle to capture diverse lighting conditions.
- Provided ground truth including 3D light direction, color intensity, ambient light, instance segmentation masks, and surface normals.
Main Results:
- SynthOutdoor dataset offers comprehensive data for 3D light direction estimation.
- The dataset includes detailed scene geometry and semantic information.
- Enables research beyond light estimation, including 3D geometry and shading.
Conclusions:
- SynthOutdoor is a valuable resource for advancing research in 3D light direction and scene understanding.
- The dataset's rich annotations facilitate broader applications in computer vision and graphics.
- Public availability of the dataset and generation software promotes further research and development.
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