mlsanityData to 7 Oct 2026

Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey

arXiv 2402.12923 ↗First posted 20 Feb 2024cs.CVcited by 3

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Abstract (arXiv)

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 (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. However, applying deep neural networks (DNNs) to process 3D PCs presents unique challenges. This paper provides an in-depth review of recent advancements in DL-based industrial CM using 3D PCs, with a specific focus on defect shape classification and segmentation within industrial applications. Recognizing the crucial role of these aspects in industrial maintenance, the paper offers insightful observations on the strengths and limitations of the reviewed DL-based PC processing methods. This knowledge synthesis aims to contribute to understanding and enhancing CM processes, particularly within the framework of remaining useful life (RUL), in industrial systems.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 111 of its 212 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

111 of 212 references.

References · 111

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38 more
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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  2. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  3. 2025FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processingcited by 0