  Visual Place Recognition (VPR) is an image-based localization method that
estimates the camera location of a query image by retrieving the most similar
reference image from a map of geo-tagged reference images. In this work, we
look into two fundamental bottlenecks for its localization accuracy: reference
map sparseness and viewpoint invariance. Firstly, the reference images for VPR
are only available at sparse poses in a map, which enforces an upper bound on
the maximum achievable localization accuracy through VPR. We therefore propose
Continuous Place-descriptor Regression (CoPR) to densify the map and improve
localization accuracy. We study various interpolation and extrapolation models
to regress additional VPR feature descriptors from only the existing
references. Secondly, we compare different feature encoders and show that CoPR
presents value for all of them. We evaluate our models on three existing public
datasets and report on average around 30% improvement in VPR-based localization
accuracy using CoPR, on top of the 15% increase by using a viewpoint-variant
loss for the feature encoder. The complementary relation between CoPR and
Relative Pose Estimation is also discussed.
