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Traditional Point Cloud Analysis

In: 3D Point Cloud Analysis

Author

Listed:
  • Shan Liu

    (Tencent Media Lab)

  • Min Zhang

    (University of Southern California)

  • Pranav Kadam

    (University of Southern California)

  • C.-C. Jay Kuo

    (University of Southern California)

Abstract

Point cloud data is widely used in the fields of computer-aided design (CAD), augmented and virtual reality (AR/VR), robot navigation and perception, and advanced driver assistance systems (ADAS). However, point cloud data is sparse, irregular, and unordered by nature. In addition, the sensor typically produces a large number (tens to hundreds of thousands) of raw data points, which brings new challenges, as many applications require real-time processing. Hence, point cloud processing is a fundamental but challenging research topic in the field of 3D computer vision. In this chapter, we will first review some basic point cloud processing algorithms for filtering, nearest neighbor searchNearest neighbor search , model fitting, feature detection, and feature description tasks. We generate some images using an open-source library, Open3D Open3D , to help illustrate the algorithms. Next, we will go over some classical pipelines for object recognition, segmentation, and registration tasks.

Suggested Citation

  • Shan Liu & Min Zhang & Pranav Kadam & C.-C. Jay Kuo, 2021. "Traditional Point Cloud Analysis," Springer Books, in: 3D Point Cloud Analysis, chapter 0, pages 15-52, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-89180-0_2
    DOI: 10.1007/978-3-030-89180-0_2
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