Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds.

从点云数据进行形状分割是工业设施数字孪生过程的核心步骤。但是，这也是一个非常费力的步骤，它抵消了结果模型的感知价值。自动执行气缸检测的最新方法可以以62％的精度和70％的召回率检测气缸，而其他形状则必须手动分割，并且无法实现形状分割。这种性能是有希望的，但远没有彻底消除手工成本。我们认为，使用类细分深度学习算法具有理论上的潜力，即在每点准确性和更少的手动细分时间方面表现更好。然而，由于缺少经过激光训练的工业形状的预训练数据集以及缺少学习这些形状的适当几何特征，因此迄今为止无法使用这种算法。在本文中，我们分三个步骤解决了这两个问题。首先，我们通过一种新颖的类细分解决方案（CLOI-NET）来解析工业点云，该类解决方案由一个优化的基于PointNET ++的深度学习网络和后处理算法组成，这些算法可对每个点实施更强的上下文关系。然后，我们允许用户通过主动学习来选择测试设施的最佳手动注释，以进一步改善结果。我们通过根据点在有意义的空间3D窗口中进行聚类来实现第一步。然后，我们应用一个类细分深度网络，并输出每个点所有标签类别的概率分布，并通过执行后处理规则来改善预测的标签。我们最终通过找到用于训练实验的最佳数据量来优化结果。我们在对工业形状建模最重要的最大注释的最大数据集上验证了我们的方法（CLOI），每点的平均准确率达到82％，所有班级的平均AUC达到95.6％，班级细分估计可节省70％的工时。这证明它是第一个在没有商业知识的情况下自动分割工业点云形状的方法，而没有先验知识，并且是在杂乱的点云中进行有效工业形状建模的基础。