Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning

TL;DR

This paper introduces Bayesian neural networks for 3D point cloud segmentation in factory environments, improving accuracy and uncertainty estimation, which enhances safety-critical applications in digital factory modeling.

Contribution

It presents a fully Bayesian and an approximate Bayesian neural network approach for point cloud segmentation, demonstrating improved performance over traditional methods.

Findings

Bayesian models outperform frequentist models in segmentation accuracy.

Incorporating uncertainty improves safety-critical decision-making.

Models perform well on both scientific and industrial datasets.

Abstract

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.