The adequate acquisition and analysis of a scene are of great interest for photogrammetry, remote sensing, computer vision, computer graphics and robotics. In this regard, 3D data have become a standard data source for numerous applications. The capability to interpret such 3D data, in turn, allows dealing with a variety of high-level tasks such as scene modelling, autonomous exploration of unknown scenes or autonomous navigation without collisions. To obtain such a capability, many applications rely on a semantic interpretation of the acquired data. In this course, we will first provide an overview of different concepts to obtain 3D data either directly by measurements or indirectly from acquired imagery. Subsequently, we focus on the semantic interpretation of 3D data. Thereby, we will present the classic approach relying on the use of handcrafted features and the state-of-the-art end-to-end learning approach relying on deep learning techniques.