To study the data-scarcity mitigation for learning-based visual localization methods via sim-to-real transfer, we curate and now present the CrossLoc benchmark datasets—a multimodal aerial sim-to-real data available for flights above nature and urban terrains. Unlike the previous computer vision datasets focusing on localization in a single domain (mostly real RGB images), the provided benchmark datasets include various multimodal synthetic cues paired to all real photos. Complementary to the paired real and synthetic data, we offer rich synthetic data that efficiently fills the flight envelope volume in the vicinity of the real data.
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