DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimation

arXiv:2402.12647v1 Announce Type: new
Abstract: This paper addresses the challenging problem of category-level pose estimation. Current state-of-the-art methods for this task face challenges when dealing with symmetric objects and when attempting to generalize to new environments solely through synthetic data training. In this work, we address these challenges by proposing a probabilistic model that relies on diffusion to estimate dense canonical maps crucial for recovering partial object shapes as well as establishing correspondences essential for pose estimation. Furthermore, we introduce …

abstract art arxiv challenges cs.cv cs.ro current data environments face modal multi-modal objects paper state symmetry synthetic synthetic data through training type uncertainty work