Visual Place Recognition in Forests with Depth-Aware Distillation

2026-06-11

AI Summary

Visual place recognition in forest environments is highly challenging due to repetitive vegetation, weak structural cues, and significant appearance variations. This work proposes a lightweight depth-aware distillation framework that, for the first time, effectively integrates geometric depth information into the DINOv2-based place recognition pipeline through knowledge distillation—without altering the pre-trained descriptor space. By introducing geometric constraints, the method enhances robustness in unstructured natural scenes where appearance alone is insufficient. Experiments on the WildCross benchmark demonstrate that the proposed approach significantly outperforms existing appearance-only methods, underscoring the critical role of depth as a complementary modality for place recognition in forested environments.

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.

Problem

• Visual Place Recognition
• Forest Environments
• Appearance Variation
• Depth Awareness
• Geometric Cues

Innovation

• depth-aware distillation
• visual place recognition
• DINOv2
• geometric cues
• forest environments