Traditional Point Cloud Analysis

Shan Liu, Min Zhang, Pranav Kadam and C.-C. Jay Kuo
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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

Chapter Chapter 2 in 3D Point Cloud Analysis, 2021, pp 15-52 from Springer

Abstract: 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.

Date: 2021
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DOI: 10.1007/978-3-030-89180-0_2

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