# Visual Place Recognition in Forests with Depth-Aware Distillation Source: [Visual Place Recognition in Forests with Depth-Aware Distillation]() Collection index: [llms.txt]() Data updated: 2026-06-13T05:00:05.486Z Authors: Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya, David Hall, Kaushik Roy, Peyman Moghadam Published: 2026-06-11T11:17:26.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Place Recognition on WildCross (inter-sequence)](): Place Recognition; WildCross (inter-sequence); Venman R1: 68.35. - [Place Recognition on WildCross Intra-Sequence](): Place Recognition; WildCross Intra-Sequence; Recall@1 (V-01): 64. ## Original sources and implementation [Original paper]() [PDF]()