  The utilization of multi-modal sensor data in visual place recognition (VPR)
has demonstrated enhanced performance compared to single-modal counterparts.
Nonetheless, integrating additional sensors comes with elevated costs and may
not be feasible for systems that demand lightweight operation, thereby
impacting the practical deployment of VPR. To address this issue, we resort to
knowledge distillation, which empowers single-modal students to learn from
cross-modal teachers without introducing additional sensors during inference.
Despite the notable advancements achieved by current distillation approaches,
the exploration of feature relationships remains an under-explored area. In
order to tackle the challenge of cross-modal distillation in VPR, we present
DistilVPR, a novel distillation pipeline for VPR. We propose leveraging feature
relationships from multiple agents, including self-agents and cross-agents for
teacher and student neural networks. Furthermore, we integrate various
manifolds, characterized by different space curvatures for exploring feature
relationships. This approach enhances the diversity of feature relationships,
including Euclidean, spherical, and hyperbolic relationship modules, thereby
enhancing the overall representational capacity. The experiments demonstrate
that our proposed pipeline achieves state-of-the-art performance compared to
other distillation baselines. We also conduct necessary ablation studies to
show design effectiveness. The code is released at:
https://github.com/sijieaaa/DistilVPR
