G2G: Exploiting Intra-Group Geometry for Inter-Group Pose Estimation

arXiv:2606.08284 · cs.CV, cs.RO · Submitted 2026-06-06 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-06-06

Updated: 2026-09-17

Project page: https://weiyufei0217.github.io/G2G

Terminology

• Accuracy — 36× in this paper · explained in 1 episode
• Alignment — 19× in this paper · explained in 1 episode
• Foundation Model — 17× in this paper · explained in 3 episodes
• Ablation — 14× in this paper · explained in 1 episode
• Calibration — 13× in this paper · explained in 7 episodes
• Aggregation — 11× in this paper · explained in 1 episode
• Robustness — 10× in this paper · explained in 5 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Sim-to-Real Transfer — 8× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Angular Error — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Graceful Degradation — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• University of Michigan North Campus Long-Term — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• LoMa: Local Feature Matching Revisited
• MapAnything: Universal Feed-Forward Metric 3D Reconstruction
• RoMa v2: Harder Better Faster Denser Feature Matching
• OpenNavMap: Multi-Session Appearance-Based Topometric Mapping for Scalable Visual Navigation
• ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization
• DINOv2: Learning Robust Visual Features without Supervision
• Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
• VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold
• Geometric Context Transformer for Streaming 3D Reconstruction
• Continuous 3D Perception Model with Persistent State
• Depth Anything 3: Recovering the Visual Space from Any Views
• $\pi^3$: Permutation-Equivariant Visual Geometry Learning

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