Virtual Gallery
Introduced by Weinzaepfel et al. in Visual Localization by Learning Objects-Of-Interest Dense Match Regression
The Virtual Gallery dataset is a synthetic dataset that targets multiple challenges such as varying lighting conditions and different occlusion levels for various tasks such as depth estimation, instance segmentation and visual localization.
It consists of a scene containing 3-4 rooms, in which a total of 42 free-for-use famous paintings are placed on the walls.
The virtual model and the captured images were generated with Unity software, allowing us to extract ground-truth information such as depth, semantic and instance segmentation, 2D-2D and 2D-3D correspondences.
Source: Visual Localization by Learning Objects-Of-Interest Dense Match RegressionPapers
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