LunarSim: A Synthetic Dataset of the LuMon Benchmark

1. Dataset Summary

The LunarSim dataset is a synthetic collection of paired stereo imagery and pixel-aligned depth maps, explicitly designed for evaluating Monocular Depth Estimation (MDE) on lunar surfaces.

It is a dataset introduced by our work LuMon, which was introduced to address the severe domain gap caused by harsh lunar shadows, textureless regolith, and the absence of atmospheric scattering. Captured within a high-fidelity Unity-based simulator, LunarSim isolates these extreme optical conditions to rigorously test the structural consistency and sim-to-real transfer capabilities of both metric and relative deep-learning architectures.

2. Dataset Structure & Details

The dataset contains approximately 3,228 synchronized image pairs (totaling ~1.9 GB), extracted from continuous simulation trajectories.

Directory Structure

• left_camera/: Contains the input images captured from the rover's left optical camera.
• depth_image/: Contains the corresponding ground-truth depth maps.

Technical Specifications

• Resolution: All images (both RGB and Depth) are 1280x720 pixels.
• Synchronization: Input images and their corresponding depth maps are perfectly paired using matching UNIX timestamps in their filenames (e.g., left_1736612498.png corresponds directly to depth_1736612498.png).
• Depth Format (Important): The depth maps are saved as 8-bit Grayscale PNGs (mode="L" in PIL/OpenCV). Pixel values are integers ranging from 0 to 255. Note that because this specific simulation split lacks an absolute metric scale, these 8-bit values represent relative (normalized) depth rather than absolute metric distances in meters.

Citation

This dataset is a subset of the LuMon Benchmark suite, accepted at the CVPR 2026 AI4Space Workshop. If you use this dataset in your research, please cite:

@inproceedings{sekmen2026lumon,
  title={LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation},
  author={Aytac Sekmen and Fatih Gunes and Furkan Horoz and Umut Isik and Alp Ozaydin and Altay Topaloglu and Umutcan Ustundas and Alp Yeni and Ersin Soken and Erol Sahin and Gokberk Cinbis and Sinan Kalkan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  year={2026}
}