Can I read 3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods on EtoBox?

3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods by Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo is a nonfiction available to read on EtoBox.

What is 3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods about?

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,

Who reads 3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods?

Author

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

Publisher

Springer International Publishing AG; MOXIC; Springer

Published

2021

Language

EN

ISBN

9783030891794

Category

nonfiction

Subjects

Computer Science, Technology, Science

Updated

2026-03-24