  Visual place recognition has gained significant attention in recent years as
a crucial technology in autonomous driving and robotics. Currently, the two
main approaches are the perspective view retrieval (P2P) paradigm and the
equirectangular image retrieval (E2E) paradigm. However, it is practical and
natural to assume that users only have consumer-grade pinhole cameras to obtain
query perspective images and retrieve them in panoramic database images from
map providers. To address this, we propose \textit{PanoVPR}, a
perspective-to-equirectangular (P2E) visual place recognition framework that
employs sliding windows to eliminate feature truncation caused by hard
cropping. Specifically, PanoVPR slides windows over the entire equirectangular
image and computes feature descriptors for each window, which are then compared
to determine place similarity. Notably, our unified framework enables direct
transfer of the backbone from P2P methods without any modification, supporting
not only CNNs but also Transformers. To facilitate training and evaluation, we
derive the Pitts250k-P2E dataset from the Pitts250k and establish YQ360, latter
is the first P2E visual place recognition dataset collected by a mobile robot
platform aiming to simulate real-world task scenarios better. Extensive
experiments demonstrate that PanoVPR achieves state-of-the-art performance and
obtains 3.8% and 8.0% performance gain on Pitts250k-P2E and YQ360 compared to
the previous best method, respectively. Code and datasets will be publicly
available at https://github.com/zafirshi/PanoVPR.
