Visual Place Recognition in Forests with Depth-Aware Distillation

Abstract:Visual place recognition in natural forest environments remains challenging due to repetitive vegetation, weak structural cues, and significant appearance variation across traversals. To address this limitation, this paper proposes a lightweight depth-aware distillation framework that injects geometric cues into a DINOv2-based place recognition model, while maintaining its pre-trained descriptor space. Evaluated on the recent WildCross benchmark, the proposed approach yields gains over an appearance-only counterpart, providing robustness to appearance variations. These results demonstrate the importance of depth as a strong complementary modality for place recognition in natural environments and identify depth-aware distillation as a promising direction for more robust forest perception.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2606.13206 [cs.CV]

(or arXiv:2606.13206v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.13206 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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