Visual Place Recognition in Forests with Depth-Aware Distillation
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 environme...
| Authors | Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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