A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

被引：0

作者：

Sarker, Sushmita ^{[1
]}

Sarker, Prithul ^{[1
]}

Stone, Gunner ^{[1
]}

Gorman, Ryan ^{[1
]}

Tavakkoli, Alireza ^{[1
]}

Bebis, George ^{[1
]}

Sattarvand, Javad ^{[2
]}

机构：

[1] Univ Nevada, Dept Comp Sci & Engn, Reno, NV 89557 USA

[2] Univ Nevada, Dept Min & Met Engn, Reno, NV USA

来源：

MACHINE VISION AND APPLICATIONS | 2024年 / 35卷 / 04期

基金：

美国国家科学基金会;

关键词：

3D classification; Computer vision; Point cloud; Semantic segmentation; NETWORKS; NET; CURVATURE;

D O I：

10.1007/s00138-024-01543-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-namely, 3D shape classification and semantic segmentation.

引用

页数：54

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