  3D Point Cloud Semantic Segmentation (PCSS) is attracting increasing
interest, due to its applicability in remote sensing, computer vision and
robotics, and due to the new possibilities offered by deep learning techniques.
In order to provide a needed up-to-date review of recent developments in PCSS,
this article summarizes existing studies on this topic. Firstly, we outline the
acquisition and evolution of the 3D point cloud from the perspective of remote
sensing and computer vision, as well as the published benchmarks for PCSS
studies. Then, traditional and advanced techniques used for Point Cloud
Segmentation (PCS) and PCSS are reviewed and compared. Finally, important
issues and open questions in PCSS studies are discussed.
