Point based Deep Learning to automate A...

While in recent years laser scanning and photogram-metry techniques have facilitated the digitalization of factory environments, the actual simulation model generation is to the greatest possible extent still manual work. In this paper we propose a cross-industry simulation model generation framework that takes in raw point clouds from laser scanners and photogram-metry and outputs a simulation model. We present

a deep learning architecture based on PointNet [1], which is capable of semantic scene segmentation, and integrate a CAD model placement routine based on unsupervised learning and point cloud registration. We prove the feasibility of our framework by collecting a scan and photogrammetry dataset at a German automotive assembly plant and applying our framework.