  We present PartNet: a consistent, large-scale dataset of 3D objects annotated
with fine-grained, instance-level, and hierarchical 3D part information. Our
dataset consists of 573,585 part instances over 26,671 3D models covering 24
object categories. This dataset enables and serves as a catalyst for many tasks
such as shape analysis, dynamic 3D scene modeling and simulation, affordance
analysis, and others. Using our dataset, we establish three benchmarking tasks
for evaluating 3D part recognition: fine-grained semantic segmentation,
hierarchical semantic segmentation, and instance segmentation. We benchmark
four state-of-the-art 3D deep learning algorithms for fine-grained semantic
segmentation and three baseline methods for hierarchical semantic segmentation.
We also propose a novel method for part instance segmentation and demonstrate
its superior performance over existing methods.
