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Efforts in various planning scenarios like factory planning, motion and trajectory planning, product design, etc. tend towards full realization in 3D. This makes point clouds an important 3D data type for capturing and assessing different situations. In this paper, we design a Bayesian extension to the frequentist PointNet classification network  by applying Bayesian convolutions and linear layers with variational inference. This approach allows the estimation of the model’s uncertainty in its predictions. Further, we are able to describe how each point in the input point cloud contributes to the prediction level uncertainty. Additionally, our network is compared against the state-of-the-art and shows strong performance. We prove the feasibility of our approach using a ModelNet 3D data set. Further, we generate an industrial 3D point data set at a German automotive assembly plant and apply our network. The results show that we can improve the frequentist baseline on ModelNet by about 6.46 %.