M3GA-Wild: A Large-Scale Dataset and Benchmark for Multi-Modal Multi-session Ground-to-Aerial Place Recognition in Forests

arXiv:2609.23003 · cs.CV, cs.RO · Submitted 2026-09-19 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-19

Updated: 2026-09-19

Code: https://github.com/MetaSLAM/GPR_Competition

Terminology

• Benchmark — 18× in this paper · explained in 2 episodes
• High Altitude — 13× in this paper
• Low Altitude — 9× in this paper
• Foundation Model — 8× in this paper · explained in 3 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Alignment — 5× in this paper · explained in 1 episode
• IEEE International Conference on Robotics and Automation — 5× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Fidelity — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Late Fusion — 1× in this paper · explained in 2 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Queensland University of Technology — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Winter Conference on Applications of Computer Vision — 1× in this paper

Sources

• CHMv2: Improvements in Global Canopy Height Mapping using DINOv3
• Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments
• HOTFLoc++: End-to-End Hierarchical LiDAR Place Recognition, Re-Ranking, and 6-DoF Metric Localisation in Forests
• WildCross: A Cross-Modal Large Scale Benchmark for Place Recognition and Metric Depth Estimation in Natural Environments
• Evaluation and Deployment of LiDAR-based Place Recognition in Dense Forests
• DINOv2: Learning Robust Visual Features without Supervision
• Wildcat: Online Continuous-Time 3D Lidar-Inertial SLAM
• Depth Anything with Any Prior

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