  The digital factory provides undoubtedly a great potential for future
production systems in terms of efficiency and effectivity. A key aspect on the
way to realize the digital copy of a real factory is the understanding of
complex indoor environments on the basis of 3D data. In order to generate an
accurate factory model including the major components, i.e. building parts,
product assets and process details, the 3D data collected during digitalization
can be processed with advanced methods of deep learning. In this work, we
propose a fully Bayesian and an approximate Bayesian neural network for point
cloud segmentation. This allows us to analyze how different ways of estimating
uncertainty in these networks improve segmentation results on raw 3D point
clouds. We achieve superior model performance for both, the Bayesian and the
approximate Bayesian model compared to the frequentist one. This performance
difference becomes even more striking when incorporating the networks'
uncertainty in their predictions. For evaluation we use the scientific data set
S3DIS as well as a data set, which was collected by the authors at a German
automotive production plant. The methods proposed in this work lead to more
accurate segmentation results and the incorporation of uncertainty information
makes this approach especially applicable to safety critical applications.
