论文标题
集体分割应用程序的合成点云生成
Synthetic Point Cloud Generation for Class Segmentation Applications
论文作者
论文摘要
由于确定基础设施退化所需的繁琐过程，维护工业设施的危害越来越大。数字双胞胎有可能通过监视基础架构的连续数字表示来改善维护。但是，绘制现有几何形状所需的时间使它们的使用效率很高。我们以前开发了类细分算法来自动化数字孪生，但是需要大量注释的点云。当前，自动分割的合成数据生成不存在。我们使用Helios ++从3D模型中自动划分点云。我们的研究有可能为有效的工业类细分铺平理由。
Maintenance of industrial facilities is a growing hazard due to the cumbersome process needed to identify infrastructure degradation. Digital Twins have the potential to improve maintenance by monitoring the continuous digital representation of infrastructure. However, the time needed to map the existing geometry makes their use prohibitive. We previously developed class segmentation algorithms to automate digital twinning, however a vast amount of annotated point clouds is needed. Currently, synthetic data generation for automated segmentation is non-existent. We used Helios++ to automatically segment point clouds from 3D models. Our research has the potential to pave the ground for efficient industrial class segmentation.
