2D-3D Match Dataset is a dataset that implements 2D-3D correspondence by leveraging several 3D datasets from RGB-D scans. The dataset uses data from SceneNN and 3DMatch. The training dataset consists of 110 RGB-D scans, 56 scenes from SceneNN and 54 scenes from 3DMatch. Given 3D points randomly sampled from a 3D point cloud, a set of 3D patches are extracted from different scan views. This dataset adopts a new method of learning local cross-domain descriptors for matching between 2D images and 3D point clouds. The dataset contains 1.4 million 2D-3D correspondences with different lighting conditions and settings obtained from public RGB-D scenes.