WAPR: A Foundation Model for Wide-Angle Refinement in Unseen Object Pose Estimation

arXiv:2610.09535 · cs.CV, cs.AI, cs.RO · Submitted 2026-10-07 · Read on arXiv

cs.CV, cs.AI, cs.RO

Submitted: 2026-10-07

Updated: 2026-10-07

Code: https://github.com/WangYuLin-SEU/WAPR

Terminology

• Transformer — 9× in this paper · explained in 2 episodes
• 6D Object Pose Estimation — 7× in this paper · explained in 1 episode
• Core — 7× in this paper · explained in 2 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• alpha — 3× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mask Generation — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Visual Correspondence Ratio — 1× in this paper

Sources

• Accurate and efficient zero-shot 6D pose estimation with frozen foundation models
• MUSE: Model-based Uncertainty-aware Similarity Estimation for zero-shot 2D Object Detection and Segmentation
• BlenderProc
• YOLOX: Exceeding YOLO Series in 2021
• Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation

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