  Modern learning-based visual feature extraction networks perform well in
intra-domain localization, however, their performance significantly declines
when image pairs are captured across long-term visual domain variations, such
as different seasonal and daytime variations. In this paper, our first
contribution is a benchmark to investigate the performance impact of long-term
variations on visual localization. We conduct a thorough analysis of the
performance of current state-of-the-art feature extraction networks under
various domain changes and find a significant performance gap between intra-
and cross-domain localization. We investigate different methods to close this
gap by improving the supervision of modern feature extractor networks. We
propose a novel data-centric method, Implicit Cross-Domain Correspondences
(iCDC). iCDC represents the same environment with multiple Neural Radiance
Fields, each fitting the scene under individual visual domains. It utilizes the
underlying 3D representations to generate accurate correspondences across
different long-term visual conditions. Our proposed method enhances
cross-domain localization performance, significantly reducing the performance
gap. When evaluated on popular long-term localization benchmarks, our trained
networks consistently outperform existing methods. This work serves as a
substantial stride toward more robust visual localization pipelines for
long-term deployments, and opens up research avenues in the development of
long-term invariant descriptors.
