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At the heart of the success of deep learning is the quality of the data.Through data augmentation, one can train models with better generalizationcapabilities and thus achieve greater results in their field of interest. Inthis work, we explore how to augment a varied set of image datasets throughrelighting so as to improve the ability of existing models to be invariant toillumination changes, namely for learned descriptors. We develop a tool, basedon an encoder-decoder network, that is able to quickly generate multiplevariations of the illumination of various input scenes whilst also allowing theuser to define parameters such as the angle of incidence and intensity. Wedemonstrate that by training models on datasets that have been augmented withour pipeline, it is possible to achieve higher performance on localizationbenchmarks.

Controllable Data Augmentation Through Deep Relighting