3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning MethodsКНИГИ » ПРОГРАММИНГ
Название: 3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods Автор: Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo Издательство: Springer Год: 2021 Формат: PDF Страниц: 156 Размер: 10 Mb Язык: English
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.
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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods 
