 The paper presents a new 3D point cloud classification benchmark data set with over 4 billion manually labeled points. The authors discuss the use of deep convolutional neural networks, CNNs, as a workhorse for 3D point cloud labeling tasks, which have shown remarkable performance improvements over state-of-the-art methods. The paper aims to close the data gap in 3D labeling tasks by providing a massive data set that consists of dense point clouds acquired with static terrestrial laser scanners. The semantic 3D.net data set contains eight semantic classes and covers a wide range of urban outdoor scenes. The authors describe their labeling interface and show that the data set provides more dense and complete point clouds with much higher overall number of labeled points compared to those already available to the research community. They also provide baseline method descriptions and comparison between methods submitted to their online system, indicating that deep learning methods in 3D point cloud labeling can learn richer, more general 3D representations. This article was authored by T. Hackel, N. Savanov, L. Lataki and others. We are article.tv, links in the description below.