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Visual place recognition (VPR) is usually considered as a specific imageretrieval problem. Limited by existing training frameworks, most deeplearning-based works cannot extract sufficiently stable global features fromRGB images and rely on a time-consuming re-ranking step to exploit spatialstructural information for better performance. In this paper, we proposeStructVPR, a novel training architecture for VPR, to enhance structuralknowledge in RGB global features and thus improve feature stability in aconstantly changing environment. Specifically, StructVPR uses segmentationimages as a more definitive source of structural knowledge input into a CNNnetwork and applies knowledge distillation to avoid online segmentation andinference of seg-branch in testing. Considering that not all samples containhigh-quality and helpful knowledge, and some even hurt the performance ofdistillation, we partition samples and weigh each sample's distillation loss toenhance the expected knowledge precisely. Finally, StructVPR achievesimpressive performance on several benchmarks using only global retrieval andeven outperforms many two-stage approaches by a large margin. After addingadditional re-ranking, ours achieves state-of-the-art performance whilemaintaining a low computational cost.

StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition