Dataset and method for deep learning-based reconstruction of 3D CAD models containing machining features for mechanical parts
- Authors
- Lee, Hyunoh; Lee, Jinwon; Kim, Hyungki; Mun, Duhwan
- Issue Date
- 30-12월-2021
- Publisher
- OXFORD UNIV PRESS
- Keywords
- 3D CAD model; 3D reconstruction; convolutional neural networks; encoder and decoder; deep learning; machining features
- Citation
- JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, v.9, no.1, pp.114 - 127
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
- Volume
- 9
- Number
- 1
- Start Page
- 114
- End Page
- 127
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/136661
- DOI
- 10.1093/jcde/qwab072
- ISSN
- 2288-4300
- Abstract
- Three-dimensional (3D) computer-aided design (CAD) model reconstruction techniques are used for numerous purposes across various industries, including free-viewpoint video reconstruction, robotic mapping, tomographic reconstruction, 3D object recognition, and reverse engineering. With the development of deep learning techniques, researchers are investigating the reconstruction of 3D CAD models using learning-based methods. Therefore, we proposed a method to effectively reconstruct 3D CAD models containing machining features into 3D voxels through a 3D encoder-decoder network. 3D CAD model datasets were built to train the 3D CAD model reconstruction network. For this purpose, large-scale 3D CAD models containing machining features were generated through parametric modeling and then converted into a 3D voxel format to build the training datasets. The encoder-decoder network was then trained using these training datasets. Finally, the performance of the trained network was evaluated through 3D reconstruction experiments on numerous test parts, which demonstrated a high reconstruction performance with an error rate of approximately 1%.
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Collections - College of Engineering > Department of Mechanical Engineering > 1. Journal Articles
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