Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光电、系统技术与图像利用研究所IOSB）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）
Chengzhi Wu, Xuelei Bi, Julius Pfrommer, Alexander Cebulla, Simon Mangold, Jürgen Beyerer
On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossible. A common strategy for solving this problem is to apply simulation-to-reality (sim2real) approaches with the help of simulated scenes. While the majority of current robotics vision sim2real work focuses on image data, we present an industrial application case that uses sim2real transfer learning for point cloud data. We provide insights on how to generate and process synthetic point cloud data in order to achieve better performance when the learned model is transferred to real-world data. The issue of imbalanced learning is investigated using multiple strategies. A novel patch-based attention network is proposed additionally to tackle this problem.
6D Pose Estimation on Point Cloud Data through Prior Knowledge Integration: A Case Study in Autonomous Disassembly
Institute for Anthropomatics and Robotics（人机动力学与机器人研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute of Production Science（生产科学研究所）；Institute of Industrial Information Technology（工业信息处理研究所）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation（光学、系统技术与图像利用弗劳恩霍夫研究所）
Fringe Projection Based Vision Pipeline for Autonomous Hard Drive Disassembly
School of Electrical and Computer Engineering, University of Georgia（佐治亚大学电气与计算机工程学院）；Alcon Research Laboratories（阿康研究实验室）；School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia（佐治亚大学环境、土木、农业和机械工程学院）
