Point Cloud-Based Deep Learning in Industrial Production: A Survey

Abstract

With the rapid development of 3D acquisition technology, point clouds have received increasing attention. In recent years, point cloud-based deep learning has been applied to various industrial scenarios, promoting industrial intelligence. However, there is still a lack of review on the application of point cloud-based deep learning in industrial production. To bridge this gap and inspire future research, this paper provides a review of current point cloud-based deep learning methods applied to industrial production from the perspective of different application scenarios, including pose estimation, defect inspection, measurement and estimation, etc. Considering the real-time requirement of industrial production, this paper also summarizes real-time point cloud-based deep learning methods in each application scenario. Then, this paper introduces commonly used evaluation metrics and public industrial point cloud datasets. Finally, from the aspects of the dataset, speed and industrial product specificity, the challenges faced by current point cloud-based deep learning methods in industrial production are discussed, and future research directions are prospected.