  Collecting real-world data is often considered the bottleneck of Artificial
Intelligence, stalling the research progress in several fields, one of which is
camera localization. End-to-end camera localization methods are still
outperformed by traditional methods, and we argue that the inconsistencies
associated with the data collection techniques are restraining the potential of
end-to-end methods. Inspired by the recent data-centric paradigm, we propose a
framework that synthesizes large localization datasets based on realistic 3D
reconstructions of the real world. Our framework, termed Synfeal: Synthetic
from Real, is an open-source, data-driven simulator that synthesizes RGB images
by moving a virtual camera through a realistic 3D textured mesh, while
collecting the corresponding ground-truth camera poses. The results validate
that the training of camera localization algorithms on datasets generated by
Synfeal leads to better results when compared to datasets generated by
state-of-the-art methods. Using Synfeal, we conducted the first analysis of the
relationship between the size of the dataset and the performance of camera
localization algorithms. Results show that the performance significantly
increases with the dataset size. Our results also suggest that when a large
localization dataset with high quality is available, training from scratch
leads to better performances. Synfeal is publicly available at
https://github.com/DanielCoelho112/synfeal.
