  Visual Place Recognition is a task that aims to predict the coordinates of an
image (called query) based solely on visual clues. Most commonly, a retrieval
approach is adopted, where the query is matched to the most similar images from
a large database of geotagged photos, using learned global descriptors. Despite
recent advances, recognizing the same place when the query comes from a
significantly different distribution is still a major hurdle for state of the
art retrieval methods. Examples are heavy illumination changes (e.g. night-time
images) or substantial occlusions (e.g. transient objects). In this work we
explore whether re-ranking methods based on spatial verification can tackle
these challenges, following the intuition that local descriptors are inherently
more robust than global features to domain shifts. To this end, we provide a
new, comprehensive benchmark on current state of the art models. We also
introduce two new demanding datasets with night and occluded queries, to be
matched against a city-wide database. Code and datasets are available at
https://github.com/gbarbarani/re-ranking-for-VPR.
