In recent years, 3D point clouds (PCs) have gained significant attention due
to their diverse applications across various fields such as computer vision
(CV), condition monitoring, virtual reality, robotics, autonomous driving etc.
Deep learning (DL) has proven effective in leveraging 3D PCs to address various
challenges previously encountered in 2D vision. However, the application of
deep neural networks (DNN) to process 3D PCs presents its own set of
challenges. To address these challenges, numerous methods have been proposed.
This paper provides an in-depth review of recent advancements in DL-based
condition monitoring (CM) using 3D PCs, with a specific focus on defect shape
classification and segmentation within industrial applications for operational
and maintenance purposes. Recognizing the crucial role of these aspects in
industrial maintenance, the paper provides insightful observations that offer
perspectives on the strengths and limitations of the reviewed DL-based PC
processing methods. This synthesis of knowledge aims to contribute to the
understanding and enhancement of CM processes, particularly within the
framework of remaining useful life (RUL), in industrial systems.

对基于深度学习处理三维点云的工业应用中的缺陷形状分类和分割的最新进展进行全面综述，旨在为产业系统中剩余寿命的理解和提升做出贡献。