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Addressing accuracy limitations and pose ambiguity in 6D object poseestimation from single RGB images presents a significant challenge,particularly due to object symmetries or occlusions. In response, we introducea novel score-based diffusion method applied to the $SE(3)$ group, marking thefirst application of diffusion models to $SE(3)$ within the image domain,specifically tailored for pose estimation tasks. Extensive evaluationsdemonstrate the method's efficacy in handling pose ambiguity, mitigatingperspective-induced ambiguity, and showcasing the robustness of our surrogateStein score formulation on $SE(3)$. This formulation not only improves theconvergence of Langevin dynamics but also enhances computational efficiency.Thus, we pioneer a promising strategy for 6D object pose estimation.

Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)