Wanli Peng

TL;DR: A self-supervised framework for category-level 6D pose estimation that leverages DeepSDF as a 3D object representation and design several novel loss functions based onDeepSDF to help the self- supervised model predict unseen object poses without any 6D object pose labels and explicit 3D models in real scenarios.

TL;DR: In this article , a category-level multi-fingered functional grasp transfer framework was proposed, in which they only need to label the hand-object contact relationship on functional parts of one object, and then transfer the contact information through the dense correspondence of functional parts between objects, so as to achieve the functional grasp synthesis for new objects based on the transferred handobject contact information.

TL;DR: TransGrasp is a category-level grasp pose estimation method that predicts grasp poses of a category of objects by labeling only one object instance, and performs grasp pose transfer across a categories of objects based on their shape correspondences.

TL;DR: The results indicate that the weight and select fusion algorithm works surprisingly well, better than all variations of the RankBoost and the StackSVM fusion algorithm.