3D point cloud learning: a survey and a toolbox

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Lu, Haoming

Contributors dc:contributor

• Shi, Humphrey

• 3D Point Cloud, Deep Learning

Statement dc:rights

• Copyright 2020 Haoming Lu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108150

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108150