COG: Confidence-aware Optimal Geometric Correspondence for Unsupervised Single-reference Novel Object Pose Estimation

Estimating the 6DoF pose of a novel object with a single reference view is challenging due to occlusions, viewpoint changes, and outliers.A core difficulty lies in finding robust cross-view correspondences, as existing methods often rely on discrete one-to-one matching that is non-differentiable and tends to collapse onto sparse keypoints.We propose Confidence-aware Optimal Geometric Correspondence (COG), an unsupervised framework that formulates correspondence estimation as a confidence-aware optimal transport problem.COG produces balanced soft correspondences by predicting point-wise confidences and injecting them as target marginals, naturally suppressing non-overlapping regions.Semantic priors from vision foundation model features further regularize the correspondences, leading to stable pose estimation.This design integrates confidence into the end-to-end correspondence finding and pose estimation pipeline, enabling fully unsupervised learning.Experiments show unsupervised COG achieves comparable performance to supervised methods, while the supervised variant outperforms them.