Preliminary Point Cloud Data Analysis for Thin-Walled Part Defect Detection based on Point Cloud Data Processing
Year: 2025
Type: article
Abstract: This paper studies the processing of point cloud data for objects, focusing on improving the bilateral filtering algorithm to address poor denoising effects by removing edge noise while preserving features. It employs voxel grid downsampling to streamline large datasets. During the point cloud registration phase, a standard plane is first fitted before registration, introducing the FPFH and SAC coarse registration and ICP fine registration algorithms to enhance registration outcomes. In the defect detection task, defect data points are extracted by calculating Euclidean distances, and the greedy projection triangulation method is used to reconstruct surfaces for visualization and quantification. This approach provides data support for subsequent work, aiding in assessing the extent of object damage.
DOI: 10.54691/18375f15
Authors: Yuan Cao, Yongxiang Jiang, Chaofan Hong, Yanxi Guo, Yuanlong Xu, Jiayi Li
Institution: Tianjin University of Technology and Education
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410712000