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Title:3D point cloud learning: a survey and a toolbox
Author(s):Lu, Haoming
Advisor(s):Shi, Humphrey
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):3D Point Cloud, Deep Learning
Abstract:The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding. However, while deep learning methods obtained remarkable success in 2D image tasks, deep models on point clouds still suffer from unique challenges in processing unstructured points with deep neural networks. This thesis reviews milestones and recent progress in different areas of point cloud learning, and proposes a uniform toolbox to help performance evaluation across models.
Issue Date:2020-05-11
Type:Thesis
URI:http://hdl.handle.net/2142/108150
Rights Information:Copyright 2020 Haoming Lu
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05


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