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3D Point Cloud Analysis

Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo (Broschiert, Englisch)

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Beschreibung

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. Weniger zeigen

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Produktinformationen

Technische Daten

Erscheinungsdatum

11.12.2022

Sprache

Englisch

EAN

9783030891824

Herausgeber

Springer International Publishing

Sonderedition

Nein

Autor

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

Seitenanzahl

146

Einbandart

Broschiert

Buch Untertitel

Traditional, Deep Learning, and Explainable Machine Learning Methods

Schlagwörter

point cloud analysis, 3D computer vision, 3D object recognition, Point cloud classification, deep learning, 3D object detection, Point cloud part segmentation, Point cloud registration, Explainable machine learning, Unsupervised learning, Machine learning, Successive subspace learning, ModelNet40, ShapeNet, PointHop, PointHop++, R-PointHop, SPA, Saab transform

Thema-Inhalt

UYQM - Maschinelles Lernen UYQ - Künstliche Intelligenz UYQP - Mustererkennung UYT - Bildverarbeitung UYQV - Maschinelles Sehen, Bildverstehen

Höhe

235 mm

Breite

15.5 cm

Hersteller: Springer, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH

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