Largest Real-World 3D Grocery Dataset for Fine-Grained Object Recognition

This paper introduces the largest real-world 3D dataset on groceries called 3DGrocery100, containing 100 fine-grained grocery classes with 87,898 point cloud instances created from 10,755 RGB-D images. The dataset enables benchmarking of state-of-the-art 3D point cloud classification models, few-shot learning, and class-incremental learning on real-world grocery data.