| 期刊全称 | 3D Point Cloud Analysis | | 期刊简称 | Traditional, Deep Le | | 影响因子2023 | Shan Liu,Min Zhang,C.-C. Jay Kuo | | 视频video | | | 发行地址 | Comprehensive investigation of point cloud processing includes traditional, deep learning, and explainable ML methods.Tackles 3D computer vision tasks (object recognition, segmentation, detection and | | 图书封面 |  | | 影响因子 | .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 orde | | Pindex | Book 2021 |
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发表于 2025-3-21 19:04:55
