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arXiv Computer Vision By Han-Gyeol Kim, JaeWan Park, Junmin Park, Darongsae Kwon

SatUnreal: A High-Precision Synthetic Dataset for Satellite Stereo Matching via Unreal Engine

Read the original on arXiv Computer Vision →

SatUnreal is a synthetic dataset created with Unreal Engine that offers 10,000 high‑resolution (0.3 m GSD) satellite stereo pairs. It addresses key limitations of existing benchmarks by ensuring physical geometry simulation, spatio‑temporal consistency, topographic diversity, and mathematically precise occlusion masks via a two‑step line‑trace algorithm. Models trained solely on SatUnreal outperform those trained on real datasets when transferred to real‑world benchmarks such as US3D and WHU‑Stereo.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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