3D point cloud analysis : traditional, deep learning, and explainable machine learning methods / Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo.
2021
TA1634 .L58 2021
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
3D point cloud analysis : traditional, deep learning, and explainable machine learning methods / Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo.
Author
Liu, Shan, author.
ISBN
9783030891800 (electronic bk.)
3030891801 (electronic bk.)
9783030891794
3030891798
3030891801 (electronic bk.)
9783030891794
3030891798
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource (xiv, 146 pages) : illustrations (chiefly color)
Other Standard Identifiers
10.1007/978-3-030-89180-0 doi
Call Number
TA1634 .L58 2021
Dewey Decimal Classification
006.3/7
Summary
This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed December 21, 2021).
Available in Other Form
3D point cloud analysis.
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
Introduction
Traditional point cloud analysis
Deep-learning-based point cloud analysis
Explainable machine learning methods for point cloud analysis
Conclusion and future work.
Traditional point cloud analysis
Deep-learning-based point cloud analysis
Explainable machine learning methods for point cloud analysis
Conclusion and future work.