EigenPlaces: Training Viewpoint Robust Models for Visual Place
  Recognition
Gabriele Berton Gabriele Trivigno Barbara Caputo Carlo Masone
Visual Place Recognition is a task that aims to predict the place of an image
(called query) based solely on its visual features. This is typically done
through image retrieval, where the query is matched to the most similar images
from a large database of geotagged photos, using learned global descriptors. A
major challenge in this task is recognizing places seen from different
viewpoints. To overcome this limitation, we propose a new method, called
EigenPlaces, to train our neural network on images from different point of
views, which embeds viewpoint robustness into the learned global descriptors.
The underlying idea is to cluster the training data so as to explicitly present
the model with different views of the same points of interest. The selection of
this points of interest is done without the need for extra supervision. We then
present experiments on the most comprehensive set of datasets in literature,
finding that EigenPlaces is able to outperform previous state of the art on the
majority of datasets, while requiring 60\% less GPU memory for training and
using 50\% smaller descriptors. The code and trained models for EigenPlaces are
available at {\small{\url{https://github.com/gmberton/EigenPlaces}}}, while
results with any other baseline can be computed with the codebase at
{\small{\url{https://github.com/gmberton/auto_VPR}}}.