데이터셋 탐색

ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships. ShapeNet Parts subset contains 31,693 meshes categorised into 16 common object classes (i.e. table, chair, plane etc.). Each shapes ground truth contains 2-5 parts (with a total of 50 part classes). Source: A review on deep learning techniques for 3D sensed data classification Image Source: ShapeNet: An Information-Rich 3D Model Repository

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.

A large-scale dataset for the point cloud completion task on the ShapeNet dataset.

The ModelNet40 zero-shot 3D classification performance of models pretrained on ShapeNet only.

MedShapeNet contains over 100,000 medical shapes, including bones, organs, vessels, muscles, etc., as well as surgical instruments. You can search, display them in 3D and download the individual shapes by using our shape search engine. Note that MedShapeNet is provided for research and educational purposes only.

ShapenetRenderer is an extension of the ShapeNet Core dataset which has more variation in camera angles. For each mesh model, the dataset provides 36 views with smaller variation and 36 views with larger variation. The resolution of the newly rendered images is 224x224 in contrast to the 137x137 original resolution. Additionally, each RGB image is paired with a depth image, a normal map and an albedo image. Source: https://github.com/Xharlie/ShapenetRender_more_variation Image Source: https://github.com/Xharlie/ShapenetRender_more_variation

The synthetic ShapeNet intrinsic image decomposition dataset used for training the deep CNN models IntrinsicNet and RetiNet of CVPR2018. See Section 4.1 of the paper for details.

The synthetic ShapeNet intrinsic image decomposition dataset of 90,000 images. 50,000 of them were used for training the deep CNN models of CVIU'2021 - see Section 4 of the paper. This is the extension of the first release of the synthetic ShapeNet intrinsic image decomposition dataset of 20,000 images used for training the deep CNN models IntrinsicNet and RetiNet of CVPR'2018. See Section 4.1 of the CVPR paper for the details of the data rendering.

The ShapeNet-Skeleton dataset has ground-truth skeleton point sets and skeletal volumes for object instances in the ShapeNet dataset. Source: https://arxiv.org/pdf/2008.05742.pdf