Few-Shot Point Cloud Classification

The ModelNet40 dataset contains synthetic object point clouds. As the most widely used benchmark for point cloud analysis, ModelNet40 is popular because of its various categories, clean shapes, well-constructed dataset, etc. The original ModelNet40 consists of 12,311 CAD-generated meshes in 40 categories (such as airplane, car, plant, lamp), of which 9,843 are used for training while the rest 2,468 are reserved for testing. The corresponding point cloud data points are uniformly sampled from the mesh surfaces, and then further preprocessed by moving to the origin and scaling into a unit sphere. Source: Geometric Feedback Network for Point Cloud Classification

ScanObjectNN is a newly published real-world dataset comprising of 2902 3D objects in 15 categories. It is a challenging point cloud classification datasets due to the background, missing parts and deformations. Source: A Self Contour-based Rotation and Translation-Invariant Transformation for Point Clouds Recognition Image Source: https://hkust-vgd.github.io/scanobjectnn/

ModelNet40-C is a comprehensive dataset to benchmark the corruption robustness of 3D point cloud recognition. We create ModelNet40-C based on the ModelNet40 validation set with 15 corruption types and 5 severity levels for each corruption type including density, noise, and transformation corruption patterns. Our dataset contains 185,000 distinct point clouds that help provide a comprehensive picture of model robustness.