  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
multihypothesis issue. In this study, we propose a novel solution by reframing
categorylevel object pose estimation as conditional generative modeling,
departing from traditional point-to-point regression. Leveraging score-based
diffusion models, we estimate object poses by sampling candidates from the
diffusion model and aggregating them through a two-step process: filtering out
outliers via likelihood estimation and subsequently mean-pooling the remaining
candidates. To avoid the costly integration process when estimating the
likelihood, we introduce an alternative method that trains an energy-based
model from the original score-based model, enabling end-to-end likelihood
estimation. Our approach achieves state-of-the-art performance on the REAL275
dataset, surpassing 50% and 60% on strict 5d2cm and 5d5cm metrics,
respectively. Furthermore, our method demonstrates strong generalizability to
novel categories sharing similar symmetric properties without fine-tuning and
can readily adapt to object pose tracking tasks, yielding comparable results to
the current state-of-the-art baselines.

Computer Vision and Pattern Recognition