论文标题
深度神经网络中的不确定性估计，用于工厂计划中的点云细分
Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning
论文作者
论文摘要
在效率和有效性方面，数字工厂无疑为未来生产系统提供了巨大的潜力。实现真实工厂数字副本的途径的一个关键方面是根据3D数据对复杂的室内环境的理解。为了生成精确的工厂模型，包括主要组件，即建筑零件，产品资产和流程详细信息，可以使用高级学习的高级方法来处理数字化过程中收集的3D数据。在这项工作中，我们提出了一个完全贝叶斯和近似贝叶斯神经网络，用于点云分割。这使我们能够分析如何估计这些网络中的不确定性的不同方法改善对RAW 3D点云的细分结果。与常见者相比，我们为贝叶斯和近似贝叶斯模型实现了卓越的模型性能。当将网络的不确定性纳入其预测中时，这种性能差异变得更加惊人。为了进行评估，我们使用科学数据集S3DIS以及一个数据集，该数据集是由德国汽车生产工厂的作者收集的。这项工作中提出的方法导致更准确的细分结果，并纳入不确定性信息使得这种方法特别适用于安全关键应用程序。
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.
