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Traditional Point Cloud Analysis

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3D Point Cloud Analysis

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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 search , model fitting, feature detection, and feature description tasks. We generate some images using an open-source library, Open3D , to help illustrate the algorithms. Next, we will go over some classical pipelines for object recognition, segmentation, and registration tasks.

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Authors and Affiliations

  1. Tencent Media Lab, Palo Alto, CA, USA

    Shan Liu

  2. University of Southern California, Los Angeles, CA, USA

    Min Zhang, Pranav Kadam & C.-C. Jay Kuo

Authors
  1. Shan Liu
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  2. Min Zhang
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  3. Pranav Kadam
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  4. C.-C. Jay Kuo
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Liu, S., Zhang, M., Kadam, P., Kuo, CC.J. (2021). Traditional Point Cloud Analysis. In: 3D Point Cloud Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-89180-0_2

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