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

PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding