  Accurate camera localization is an essential part of tracking systems.
However, localization results are greatly affected by illumination. Including
data collected under various lighting conditions can improve the robustness of
the localization algorithm to lighting variation. However, this is very tedious
and time consuming. By using synthesized images it is possible to easily
accumulate a large variety of views under varying illumination and weather
conditions. Despite continuously improving processing power and rendering
algorithms, synthesized images do not perfectly match real images of the same
scene, i.e. there exists a gap between real and synthesized images that also
affects the accuracy of camera localization. To reduce the impact of this gap,
we introduce "REal-to-Synthetic Transform (REST)." REST is an autoencoder-like
network that converts real features to their synthetic counterpart. The
converted features can then be matched against the accumulated database for
robust camera localization. In our experiments REST improved feature matching
accuracy under variable lighting conditions by approximately 30%. Moreover, our
system outperforms state of the art CNN-based camera localization methods
trained with synthetic images. We believe our method could be used to
initialize local tracking and to simplify data accumulation for lighting robust
localization.
