Due to the easy accessibility of the 3D metrology tools such as Coordinate Measuring Machine or scanning tools, structured point cloud data is becoming more and more popular. Therefore, modeling the structured point cloud is an important task in many application domains. We model the structure point cloud as tensor and propose regularized tucker decomposition and regularized tensor regression to detect the variation patterns in the data and link these patterns to the process variables. Furthermore, the performance of the proposed method is evaluated through simulation and a real case study in the point cloud data in the turning process.
Structured Point Cloud Data Modeling Via Regularized Tensor Decomposition And Regression
PACELLA, Massimo;
2016-01-01
Abstract
Due to the easy accessibility of the 3D metrology tools such as Coordinate Measuring Machine or scanning tools, structured point cloud data is becoming more and more popular. Therefore, modeling the structured point cloud is an important task in many application domains. We model the structure point cloud as tensor and propose regularized tucker decomposition and regularized tensor regression to detect the variation patterns in the data and link these patterns to the process variables. Furthermore, the performance of the proposed method is evaluated through simulation and a real case study in the point cloud data in the turning process.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
