TPDNet: A point cloud data denoising method for offshore drilling platforms and its application

被引：1

作者：

Ran, Chunqing ^{[1
]}

Zhang, Xiaobo ^{[1
,2
]}

Han, Shuo ^{[1
]}

Yu, Hao ^{[1
]}

Wang, Shengli ^{[1
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Ocean Sci & Engn, Qingdao 266590, Peoples R China

[2] Qingdao Marine Sci & Technol Ctr, Lab Marine Geol, Qingdao 266237, Peoples R China

来源：

MEASUREMENT | 2025年 / 241卷

基金：

中国国家自然科学基金;

关键词：

Deep learning; Self-attention mechanism; Offshore drilling platforms; Point cloud denoising; 3D surface reconstruction; OIL; PROJECTION; NETWORK;

D O I：

10.1016/j.measurement.2024.115671

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The complex working environment of offshore drilling platforms makes the acquisition of point cloud data susceptible to noise pollution. To address this issue, this paper proposes a denoising network for point cloud data of offshore drilling platforms, called TPDNet. TPDNet utilizes the feature abstraction module to aggregate local features in point clouds and employs a self-attention mechanism for feature extraction, thereby enabling the effective identification of noisy point clouds. This paper also presents an offshore drilling platform point cloud dataset for training and testing deep learning models. It demonstrates the reconstruction of 3D surfaces of equipment on offshore drilling platforms using the target point cloud data obtained by TPDNet. The result validates the practicality of TPDNet. Consequently, this paper provides technical support for point cloud data processing, which has promising practical applications in the field of ocean engineering.

引用

页数：16