  Within the past decade, the rise of applications based on artificial
intelligence (AI) in general and machine learning (ML) in specific has led to
many significant contributions within different domains. The applications range
from robotics over medical diagnoses up to autonomous driving. However, nearly
all applications rely on trained data. In case this data consists of 3D images,
it is of utmost importance that the labeling is as accurate as possible to
ensure high-quality outcomes of the ML models. Labeling in the 3D space is
mostly manual work performed by expert workers, where they draw 3D bounding
boxes around target objects the ML model should later automatically identify,
e.g., pedestrians for autonomous driving or cancer cells within radiography.
  While a small range of recent 3D labeling tools exist, they all share three
major shortcomings: (i) they are specified for autonomous driving applications,
(ii) they lack convenience and comfort functions, and (iii) they have high
dependencies and little flexibility in data format. Therefore, we propose a
novel labeling tool for 3D object detection in point clouds to address these
shortcomings.
