UE4-Stereo

Dataset Description

UE4-Stereo is a synthetic stereo vision dataset generated in Unreal Engine 4.27 using the Microsoft AirSim simulation framework. The dataset provides synchronized stereo RGB images, dense ground-truth depth maps, and full 6-DoF camera poses. It is designed to support research and benchmarking in stereo matching, visual odometry, and visual SLAM, particularly in controlled indoor environments.

The dataset focuses on realistic indoor scenes with structural elements such as walls, pipes, pillars, industrial components, and furniture, while allowing precise control over camera configuration and motion dynamics.

The dataset contains multiple trajectory configurations, stereo baselines, and camera geometries, enabling evaluation under different acquisition conditions while preserving fully synchronized ground-truth information.

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Dataset Overview

• Number of scenes: 4 indoor scenes
• Number of sequences: 32 acquisition sequences (8 per scene)
• Total stereo image pairs: 36,838
• Total RGB images: 73,676
• Trajectory type: Closed-loop trajectories
• Sampling frequency: 3 Hz and 10 Hz
• Drone velocity: 1.5 m/s

Each sequence corresponds to a complete loop, where the drone takes off, follows a predefined trajectory, and lands at the same initial position.

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Acquisition Setup

• Simulation engine: Unreal Engine 4.27
• Simulation framework: Microsoft AirSim
• Platform: Virtual drone
• Cameras: Stereo RGB cameras
• Image resolution: 640 × 480 pixels
• Camera intrinsics: Fixed across all sequences
• Stereo baseline: 24 cm and 32 cm (depending on the sequence)
• Camera configuration: Parallel and convergent (±5°) setups

Drone motion and data acquisition were controlled via Python scripts using the AirSim API. All data streams are temporally synchronized and referenced to the left camera.

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Scenes and Sequences

Scene 1

Scene 1 represents an indoor industrial-like environment. It contains eight sequences with controlled variations in:

• Loop direction (clockwise and counter-clockwise)
• Lateral drone displacement (~1 m)
• Stereo baseline (24 cm and 32 cm)
• Camera convergence (parallel and ±5° convergent)

The camera orientation remains fixed across all sequences in this scene.

Scene 2

Scene 2 represents a second indoor environment with similar structural complexity. It also contains eight sequences with variations in:

• Loop direction (clockwise and counter-clockwise)
• Lateral drone displacement (~1 m)
• Stereo baseline (24 cm and 32 cm)
• Camera convergence (parallel and ±5° convergent)

Scene 2 introduces additional viewpoint and structural variations relative to Scene 1.

Scene 3

Scene 3 follows the same acquisition protocol adopted in Scene 1 while introducing smoother trajectory transitions and slight environmental variations to increase motion diversity and temporal continuity.

The same trajectory variations are preserved:

• Loop direction (clockwise and counter-clockwise)
• Lateral drone displacement (~1 m)
• Stereo baseline (24 cm and 32 cm)
• Camera convergence (parallel and ±5° convergent)

Scene 3 contains denser temporal sampling and longer trajectories compared to Scene 1.

Scene 4

Scene 4 follows the same acquisition protocol adopted in Scene 2 while introducing smoother trajectory transitions and slight environmental variations to increase motion diversity and temporal continuity.

The same trajectory variations are preserved:

• Loop direction (clockwise and counter-clockwise)
• Lateral drone displacement (~1 m)
• Stereo baseline (24 cm and 32 cm)
• Camera convergence (parallel and ±5° convergent)

Scene 4 contains denser temporal sampling and longer trajectories compared to Scene 2.

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Sequence Statistics

Scene	Stereo Pairs
Scene 1	4,407
Scene 2	5,623
Scene 3	12,317
Scene 4	14,491
Total	36,838

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Data Organization

The dataset is organized as follows:

scene_1/
  readme_scene1.txt
  sequence01/
    left_rgb/
    right_rgb/
    depth_npy/
    depth_visual/
    associate_rgb.txt
    associate_rgbd.txt
    groundtruth.txt
  sequence02/
    ...
  ...
  sequence08/

scene_2/
  ...

scene_3/
  readme_scene1.txt
  sequence01/
    left_rgb/
    right_rgb/
    depth_npy/
    associate_rgb.txt
    associate_rgbd.txt
    groundtruth.txt
  sequence02/
    ...
  ...
  sequence08/

scene_4/
  ...

README.md

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## Data Formats

### RGB Images
- Format: PNG  
- Resolution: 640 × 480  
- Channels: RGB (8-bit)  
- Stored separately for left and right cameras  

### Depth Maps
- **Metric depth:** NumPy arrays (`.npy`)  
  - Data type: float32  
  - Units: meters  
  - Resolution: 640 × 480  
  - Pixel-wise aligned with the left RGB image  

- **Depth visualization:** PNG images  
  - 8-bit grayscale  
  - Normalized for visualization only  
  - Not suitable for quantitative evaluation  

### Camera Poses
- Format: Text (`groundtruth.txt`)  
- Pose representation:  

timestamp tx ty tz qx qy qz qw

- Position: (tx, ty, tz) in meters  
- Orientation: quaternion (qx, qy, qz, qw)  
- Coordinate system: Unreal Engine world frame  

The pose format follows the standard convention used by the TUM RGB-D dataset.

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## Association Files
Each sequence provides:
- **associate_rgb.txt:** Synchronization between left and right RGB images  
- **associate_rgbd.txt:** Synchronization between left RGB images and depth maps  

These files guarantee one-to-one correspondence between all data modalities.

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## Intended Use
This dataset is intended for:
- Stereo disparity estimation  
- Visual odometry  
- Visual SLAM  
- Domain adaptation and synthetic-to-real transfer  
- Evaluation of robustness to camera geometry variations  

**Note:** This is not a classification dataset. Folder names and file organization should not be interpreted as class labels.

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## License
This dataset is released under the **Creative Commons Attribution 4.0 (CC BY 4.0)** license.

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## Citation
If you use this dataset, please cite the corresponding **Data in Brief** article (under review).  
The citation will be updated upon acceptance.