GenPose: Generative Category-level Object Pose Estimation via Diffusion Models

Object pose estimation plays a vital role in embodied AI and computer vision, enabling intelligent agents to comprehend and interact with their surroundings. Despite the practicality of category-level pose estimation, current approaches encounter challenges with partially observed point clouds, known as the multi-hypothesis issue. In this study, we propose a novel solution by reframing category-level object pose estimation as conditional generative modeling, departing from traditional point-to-point regression.