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<p>As a significant means of representing 3D scenes, point clouds are extensively utilized in various fields Such as computer vision, autonomous driving, robotic interaction, and urban modeling. While deep learning has achieved remarkable Success in the realm of two-dimensional images, and its application to three-dimensional point clouds is also progressively gaining traction. However, the irregular and unstructured nature of point cloud data presents numerous challenges when applying deep learning algorithms to these 3D representations. To foster future research endeavors, this paper concentrates on three fundamental tasks associated with point clouds: classification, object detection, and semantic segmentation. It systematically reviews the current state of development regarding deep learning algorithms pertinent to these tasks. By organizing and analyzing existing literature alongside experimental results derived from publicly available datasets, this paper compares the strengths of different methodologies while also highlighting their limitations. Ultimately, it summarizes the technical challenges encountered in advancing deep learning algorithms for point clouds and outlines potential avenues for progress within this domain.</p>

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Deep learning techniques for point cloud tasks: a review

  • 期刊论文

  • 发表日期：

    2025年9月30日

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  • Xiaona Song,
  • Haozhe Zhang,
  • Lijun Wang,
  • Jinxing Niu,
  • Ying Zhu,
  • Junjie Nian,
  • Ruixue Cheng

摘要

As a significant means of representing 3D scenes, point clouds are extensively utilized in various fields Such as computer vision, autonomous driving, robotic interaction, and urban modeling. While deep learning has achieved remarkable Success in the realm of two-dimensional images, and its application to three-dimensional point clouds is also progressively gaining traction. However, the irregular and unstructured nature of point cloud data presents numerous challenges when applying deep learning algorithms to these 3D representations. To foster future research endeavors, this paper concentrates on three fundamental tasks associated with point clouds: classification, object detection, and semantic segmentation. It systematically reviews the current state of development regarding deep learning algorithms pertinent to these tasks. By organizing and analyzing existing literature alongside experimental results derived from publicly available datasets, this paper compares the strengths of different methodologies while also highlighting their limitations. Ultimately, it summarizes the technical challenges encountered in advancing deep learning algorithms for point clouds and outlines potential avenues for progress within this domain.