Hyperspectral imaging has been extensively studied in remote sensing. In this community, several approaches exist for classifying different organic and an-organic materials. However, this data is usually collected from large distances (flight or satellite data) and hence lacks geometric precision, which is required for robotic applications like mapping and navigation. In this paper, we present a reference data set that maps hyperspectral intensity data to a terrestrial 3D laser scanner to generate what we call hyperspectral point clouds (HPCs). To organize and distribute the resulting massive data, we designed an HDF5 file structure that is the basis to feed information derived from the raw data into robot control frameworks like ROS.