NDLI: Dense Robust 3D Reconstruction and Measurement for 3D Printing Process Based on Vision

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Dense Robust 3D Reconstruction and Measurement for 3D Printing Process Based on Vision

Content Provider	MDPI
Author	Lv, Ning Wang, Chengyu Qiao, Yujing Zhang, Yongde
Copyright Year	2021
Description	The 3D printing process lacks real-time inspection, which is still an open-loop manufacturing process, and the molding accuracy is low. Based on the 3D reconstruction theory of machine vision, in order to meet the applicability requirements of 3D printing process detection, a matching fusion method is proposed. The fast nearest neighbor (FNN) method is used to search matching point pairs. The matching point information of FFT-SIFT algorithm based on fast Fourier transform is superimposed with the matching point information of AKAZE algorithm, and then fused to obtain more dense feature point matching information and rich edge feature information. Combining incremental SFM algorithm with global SFM algorithm, an integrated SFM sparse point cloud reconstruction method is developed. The dense point cloud is reconstructed by PMVs algorithm, the point cloud model is meshed by Delaunay triangulation, and then the accurate 3D reconstruction model is obtained by texture mapping. The experimental results show that compared with the classical SIFT algorithm, the speed of feature extraction is increased by 25.0%, the number of feature matching is increased by 72%, and the relative error of 3D reconstruction results is about 0.014%, which is close to the theoretical error.
Starting Page	7961
e-ISSN	20763417
DOI	10.3390/app11177961
Journal	Applied Sciences
Issue Number	17
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2021-08-28
Access Restriction	Open
Subject Keyword	Applied Sciences Industrial Engineering Manufacturing Engineering 3d Printing Vision Inspection 3d Reconstruction Feature Matching Fft-sift-akaze Integrated Sfm
Content Type	Text
Resource Type	Article

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