  Visual place recognition (VPR) is usually considered as a specific image
retrieval problem. Limited by existing training frameworks, most deep
learning-based works cannot extract sufficiently stable global features from
RGB images and rely on a time-consuming re-ranking step to exploit spatial
structural information for better performance. In this paper, we propose
StructVPR, a novel training architecture for VPR, to enhance structural
knowledge in RGB global features and thus improve feature stability in a
constantly changing environment. Specifically, StructVPR uses segmentation
images as a more definitive source of structural knowledge input into a CNN
network and applies knowledge distillation to avoid online segmentation and
inference of seg-branch in testing. Considering that not all samples contain
high-quality and helpful knowledge, and some even hurt the performance of
distillation, we partition samples and weigh each sample's distillation loss to
enhance the expected knowledge precisely. Finally, StructVPR achieves
impressive performance on several benchmarks using only global retrieval and
even outperforms many two-stage approaches by a large margin. After adding
additional re-ranking, ours achieves state-of-the-art performance while
maintaining a low computational cost.
