  The task of Visual Place Recognition (VPR) is to predict the location of a
query image from a database of geo-tagged images. Recent studies in VPR have
highlighted the significant advantage of employing pre-trained foundation
models like DINOv2 for the VPR task. However, these models are often deemed
inadequate for VPR without further fine-tuning on task-specific data. In this
paper, we propose a simple yet powerful approach to better exploit the
potential of a foundation model for VPR. We first demonstrate that features
extracted from self-attention layers can serve as a powerful re-ranker for VPR.
Utilizing these features in a zero-shot manner, our method surpasses previous
zero-shot methods and achieves competitive results compared to supervised
methods across multiple datasets. Subsequently, we demonstrate that a
single-stage method leveraging internal ViT layers for pooling can generate
global features that achieve state-of-the-art results, even when reduced to a
dimensionality as low as 128D. Nevertheless, incorporating our local foundation
features for re-ranking, expands this gap. Our approach further demonstrates
remarkable robustness and generalization, achieving state-of-the-art results,
with a significant gap, in challenging scenarios, involving occlusion,
day-night variations, and seasonal changes.
