6D pose estimation

In this work, we propose a few-shot pose estimation (FSPE) approach called SA6D, which uses a self-adaptive segmentation module to identify the novel target object and construct a point cloud model of the target object using only a small number of cluttered reference images.

We introduce a novel meta-learning framework for 6D pose estimation with strong generalization ability on unseen objects within and across object categories, with a proposed GNN-based keypoint prediction module that leverages geometric information from canonical keypoint coordinates and captures local spatial constraints among keypoints via message passing.