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Modern learning-based visual feature extraction networks perform well inintra-domain localization, however, their performance significantly declineswhen image pairs are captured across long-term visual domain variations, suchas different seasonal and daytime variations. In this paper, our firstcontribution is a benchmark to investigate the performance impact of long-termvariations on visual localization. We conduct a thorough analysis of theperformance of current state-of-the-art feature extraction networks undervarious domain changes and find a significant performance gap between intra-and cross-domain localization. We investigate different methods to close thisgap by improving the supervision of modern feature extractor networks. Wepropose a novel data-centric method, Implicit Cross-Domain Correspondences(iCDC). iCDC represents the same environment with multiple Neural RadianceFields, each fitting the scene under individual visual domains. It utilizes theunderlying 3D representations to generate accurate correspondences acrossdifferent long-term visual conditions. Our proposed method enhancescross-domain localization performance, significantly reducing the performancegap. When evaluated on popular long-term localization benchmarks, our trainednetworks consistently outperform existing methods. This work serves as asubstantial stride toward more robust visual localization pipelines forlong-term deployments, and opens up research avenues in the development oflong-term invariant descriptors.

Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences