  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, a novel
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 will be publicly
available at https://liuliu66.github.io/articulationobjects/.
