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. 

The synthetic data rendering was achieved using the proposed data generation workflow TOPO-DataGen. The provided CrossLoc datasets were used as an initial benchmark to showcase the use of synthetic data to assist visual localization in the real world with limited real data.

Please refer to our main paper at https://arxiv.org/abs/2112.09081 and our code at https://github.com/TOPO-EPFL/CrossLoc for details.

The dataset collection, processing, and validation details are explained in our paper available at https://arxiv.org/abs/2112.09081 and our code available at https://github.com/TOPO-EPFL/CrossLoc. 

Please refer to our paper available at https://arxiv.org/abs/2112.09081 and our code available at https://github.com/TOPO-EPFL/CrossLoc for further details regarding the use of the datasets.