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

What is it about?

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.

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Photo by Lenny Kuhne on Unsplash

Photo by Lenny Kuhne on Unsplash

Why is it important?

Point cloud-based deep learning methods have been applied to various real-world scenarios. However, there is still a lack of survey that reviews point cloud-based deep learning methods applied in industrial production. At present, the application of point cloud-based deep learning in industrial production is in its early stage, and it is necessary to review the existing methods to inspire future research. Therefore, from the perspective of different application scenarios, this paper reviews current point cloud-based deep learning methods applied in industrial production.