Journal:IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Key Words:robotics, articulated object, 6D pose estimation, robot manipulation

Abstract:Human life is populated with articulated objects. A comprehensive understanding of articulated objects, namely appearance, structure, physics property, and semantics, will benefit many research communities. As current articulated object understanding solutions are usually based on synthetic object dataset with CAD models without physics properties, which prevent satisfied generalization from simulation to real-world applications in visual and robotics tasks. To bridge the gap, we present AKB-48: a large-scale Articulated object Knowledge Base which consists of 2,037 real-world 3D articulated object models of 48 categories. Each object is described by a knowledge graph ArtiKG. To build the AKB-48, we present a fast articulation knowledge modeling (FArM) pipeline, which can fulfill the ArtiKG for an articulated object within 10-15 minutes, and largely reduce the cost for object modeling in the real world. Using our dataset, we propose AKBNet, an integral pipeline for Category-level Visual Articulation Manipulation (C-VAM) task, in which we benchmark three sub-tasks, namely pose estimation, object reconstruction and manipulation. Dataset, codes, and models are publicly available at https://liuliu66. github. io/AKB-48.

Co-author:Yang Han,QIaojun Yu,Sucheng Qian,Haoyuan Fu,Wenqiang Xu

First Author:Liu Liu

Indexed by:CCF A类会议

Correspondence Author:Cewu Lu

Page Number:14809-14818

Translation or Not:no

Date of Publication:2022-06-28