基于深度学习的三维点云语义分割方法研究

doi:10.3778/j.issn.1002-8331.2107-0142

摘要/Abstract

摘要：

综述了基于深度学习的三维点云语义分割方法的研究进展。利用文献分析法，按照数据的表现形式对基于深度学习的三维点云语义分割的方法进行阐述。探讨了近些年的国内外发展现状，分析了目前相关方法的优缺点，并展望了未来发展的趋势。深度学习的加入在点云语义分割技术研究上发挥着越来越重要的作用，推动了制造与包装等领域趋向于智能信息化。根据各类方法的优缺点，利用深度学习技术构建出基于投影、体素、多视图以及直接基于点云的2D-3D组合语义分割框架模型是未来的一个重要研究方向。

关键词: 计算机视觉, 智能包装, 深度学习, 三维点云, 语义分割

Abstract:

This paper summarizes the methods of deep learning-based semantic segmentation for 3D point cloud. The literature research method is used to describe deep learning-based semantic segmentation methods for 3D point cloud according to the representation of data. It discusses the current situation of domestic and foreign development in recent years, and analyzes the advantages and disadvantages of the current related methods, and prospects the future development trend. Deep learning plays an extremely important role in the research of semantic segmentation technology for point cloud, and promotes the manufacturing, packaging fields and etc to development in the direction of intelligence. According to the advantages and disadvantages of various methods, it is an important research direction to construct a framework model of semantic segmentation combined with 2D-3D for projection, voxel, multi-view and point cloud in the future.

Key words: computer vision, intelligent packaging, deep learning, 3D point cloud, semantic segmentation

王涛，王文举，蔡宇. 基于深度学习的三维点云语义分割方法研究[J]. 计算机工程与应用, 2021, 57(23): 18-26.

WANG Tao, WANG Wenju, CAI Yu. Research of Deep Learning-Based Semantic Segmentation for 3D Point Cloud[J]. Computer Engineering and Applications, 2021, 57(23): 18-26.

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