The rapid development of 3D sensors and object detection methods based on 3D point clouds has led to increasing demand for labeling tools that provide suitable training data. However, existing labeling tools mostly focus on a single use case and generate bounding boxes only indirectly from a selection of points, which often impairs the label quality. Therefore, this work describes labelCloud, a generic point cloud labeling tool that can process all common file formats and provides 3D bounding boxes in multiple label formats. labelCloud offers two labeling methods that let users draw rotated bounding boxes directly inside the point cloud. Compared to a labeling tool based on indirect labeling, labelCloud could significantly increase the label precision while slightly reducing the labeling time. Due to its modular architecture, researchers and practitioners can adapt the software to their individual needs. With labelCloud, we contribute to enabling convenient 3D vision research in novel application domains.