ShapeNet is an ongoing effort to establish a richly-annotated, large-scale dataset of 3D shapes. We provide researchers around the world with this data to enable research in computer graphics, computer vision, robotics, and other related disciplines. ShapeNet is a collaborative effort between researchers at Princeton, Stanford and TTIC.
ShapeNet consists of several subsets:
ShapeNetCore is a subset of the full ShapeNet dataset with single clean 3D models and manually verified category and alignment annotations. It covers 55 common object categories with about 51,300 unique 3D models. The 12 object categories of PASCAL 3D+, a popular computer vision 3D benchmark dataset, are all covered by ShapeNetCore.
ShapeNetSem is a smaller, more densely annotated subset consisting of 12,000 models spread over a broader set of 270 categories. In addition to manually verified category labels and consistent alignments, these models are annotated with real-world dimensions, estimates of their material composition at the category level, and estimates of their total volume and weight.
March, 2019 We are happy to announce the prelease of PartNet v0. PartNet provides fine-grained, hierarchical part annotations from ShapeNet..
Oct, 2016 Training, validation and test splits for ShapeNetCore are available in CSV format HERE. These splits were used for the SHREC16 3D shape retrieval contest (see www.shapenet.org/shrec16 for more information).