Three-dimensional reconstruction of industrial parts from a single image

作     者：Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao 

作者机构：School of Mechanical Engineering and AutomationBeihang UniversityBeijing 100191China Key Laboratory of Aeronautics Smart ManufacturingBeihang UniversityBeijing 100191China State Key Laboratory of Computer ScienceInstitute of SoftwareChinese Academy of SciencesBeijing 100190China 

出 版 物：《Visual Computing for Industry,Biomedicine,and Art》 (工医艺的可视计算(英文))

年 卷 期：2024年第7卷第1期

页      面：340-351页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Aeronautical Science Foundation of China,No.2023Z068051002 2021 Special Scientific Research on Civil Aircraft Project the Natural Science Foundation of China,Nos.61572056 and 61872347 the Special Plan for the Development of Distinguished Young Scientists of ISCAS,No.Y8RC535018 

主　　题：Three-dimensional reconstruction Non-uniform rational B-splines Industrial parts Deep learning 

摘      要：This study proposes an image-based three-dimensional(3D)vector reconstruction of industrial parts that can gener-ate non-uniform rational B-splines(NURBS)surfaces with high fidelity and *** contributions of this study include three parts:first,a dataset of two-dimensional images is constructed for typical industrial parts,including hex-agonal head bolts,cylindrical gears,shoulder rings,hexagonal nuts,and cylindrical roller bearings;second,a deep learning algorithm is developed for parameter extraction of 3D industrial parts,which can determine the final 3D parameters and pose information of the reconstructed model using two new nets,CAD-ClassNet and CAD-ReconNet;and finally,a 3D vector shape reconstruction of mechanical parts is presented to generate NURBS from the obtained shape *** final reconstructed models show that the proposed approach is highly accurate,efficient,and practical.