A Pre-Procession Module for Point-Based Deep Learning in Dense Point Clouds in the Ship Engineering Field
A Pre-Procession Module for Point-Based Deep Learning in Dense Point Clouds in the Ship Engineering Field
复制标题
DOI：
10.3390/jmse11122248
复制
发表时间：
2023-11
影响因子：
2.9
通讯作者：
Shilin Huo;Yujun Liu;Ji Wang;Rui Li;Xiao Liu;Jiawei Shi
中科院分区：
文献类型：
--
作者：
Shilin Huo;Yujun Liu;Ji Wang;Rui Li;Xiao Liu;Jiawei Shi
近年来，点云技术在船舶工程领域得到了应用。然而，陆地激光扫描（TLS）技术在船舶工程应用中获取的密集点云给一些强大而先进的基于点的深度学习点云处理方法带来了障碍。本文提出了一种深度学习预处理模块，以确保在常用计算机设备上处理密集点云的可行性。根据传统的点云处理方法和PointNet++范式设计了点云预处理模块，并在两个船舶结构数据集和两个流行的点云数据集上进行了评估。实验结果表明，（i）该模块提高了基于点的深度学习语义分割网络的性能，（ii）该模块使现有的基于点的深度学习网络具有处理密集输入点云的能力。所提出的模块可以为各种工业应用中的真实密集点云提供有用的语义分割工具。
Recently, point cloud technology has been applied in the ship engineering field. However, the dense point cloud acquired by terrestrial laser scanning (TLS) technology in ship engineering applications brings an obstacle to some powerful and advanced point-based deep learning point cloud processing methods. This paper presents a deep learning pre-procession module to ensure the feasibility of processing dense point clouds on commonly available computer devices. The pre-procession module is designed according to the traditional point cloud processing methods and the PointNet++ paradigm, and is evaluated on two ship structure datasets and two popular point cloud datasets. Experimental results illustrate that (i) the proposed module improves the performance of point-based deep learning semantic segmentation networks, and (ii) the proposed module empowers the existing point-based deep learning networks with the capability to process dense input point clouds. The proposed module may provide a useful semantic segmentation tool for realistic dense point clouds in various industrial applications.