PartCrafter's core competency stems in part from its large training dataset, which encompasses two key dimensions: at the quantitative level, the scale of 130,000 3D objects far exceeds that of comparable tools (e.g., ShapeNet's 50,000 magnitude); and at the qualitative level, the 100,000 samples with part annotations provide fine-grained supervised signals. The dataset covers 20 product categories such as machine parts, furniture, and electronic devices, and each model is decomposed into 7.3 semantic parts on average. This data strength translates directly into three practical properties: the first is generalization ability to handle unseen object classes; the second is detail reduction, which accurately generates standard connection structures (e.g., mortise and tenon, thread); and the third is scene comprehension, which supports speculation of the complete structure from heavily occluded images. The project plans to publicly release access to these resources by July 2025.
This answer comes from the articlePartCrafter: Generating Editable 3D Part Models from a Single ImageThe
