UniPose9D: Universal Category-Agnostic Object Pose Estimation

Jul 14, 2026

UniPose9D is a foundation model for 9D object pose estimation that operates without relying on category labels, CAD models, or reference views. It leverages point pairs and introduces a novel RANSAC N-hop Kabsch-Umeyama algorithm to predict rotation, translation, and metric size from RGB-D input or RGB images with predicted depth. The model demonstrates performance that matches or surpasses specialist methods across six datasets and shows strong generalization to unseen objects and real-world scenarios.

Why it matters: This work represents a significant advance in generalizable 3D vision, enabling robust pose estimation for arbitrary objects without category-specific training, which is important for robotics and augmented reality.

Full story at: arXiv Computer Vision ↗