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In computer vision and computer graphics, segmenting three-dimensional (3D) scenes is an important and difficult subject. The goal of 3D segmentation is to develop computational approaches that anticipate the fine-grained labels of objects in a 3D scene for a variety of applications, such as autonomous driving, industrial control, mobile robots, medical image analysis, and augmented reality. In this study, we explore the most popular approaches and datasets used in 3D semantic segmentation, emphasizing their benefits and limitations.

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3D Semantic Segmentation from LiDAR Point Clouds: A Review

  • 会议论文

  • 发表日期：

    2025年

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  • Adnan Anouzla,
  • Mohamed Bakali El Mohamadi,
  • Nabila Zrira,
  • Khadija Ouazzani-Touhami

摘要

In computer vision and computer graphics, segmenting three-dimensional (3D) scenes is an important and difficult subject. The goal of 3D segmentation is to develop computational approaches that anticipate the fine-grained labels of objects in a 3D scene for a variety of applications, such as autonomous driving, industrial control, mobile robots, medical image analysis, and augmented reality. In this study, we explore the most popular approaches and datasets used in 3D semantic segmentation, emphasizing their benefits and limitations.