UF0-6D: Unified Flow-based Zero-Shot 6D Object Pose Estimation without Refinement

Existing zero-shot 6D object pose estimation methods predominantly rely on multi-stage pipelines with iterative render-and-compare refinement or coarse-to-fine hypothesis selection, leading to substantial computational overhead and implementation complexity. Generative modeling on geometric manifolds offers an attractive alternative by directly representing pose posteriors. In this work, we present UF0-6D, a Unified Flow-based Zero-shot framework for 6D object pose estimation without refinement. UF0-6D reformulates pose estimation as conditional Riemannian flow matching on SE(3), learning an instance-conditioned posterior via geodesic-consistent bridge velocities and symmetry-aware Riemannian optimal transport supervision. A single ODE integration pass replaces coarse-to-refine cascades and iterative alignment, unifying modelbased and model-free settings via a geometry-conditioning design that maps both CAD renders and reference-view renders into a shared pointtoken representation. Our RGB-D variant achieves a mean AR of 81.2 on BOP benchmark datasets, surpassing the previous state-of-the-art by +6.0 AR, while our RGB variant reaches 70.6 AR at only 0.379s per image, establishing a new accuracy–speed Pareto frontier.