Reverse engineering Computer-Aided Design (CAD) models based on the original geometry is a valuable and challenging research problem that has numerous applications across various tasks. However, previous approaches have often relied on excessive manual interaction, leading to limitations in reconstruction speed. To mitigate this issue, in this study, we approach the reconstruction of a CAD model by sequentially constructing geometric primitives (such as vertices, edges, loops, and faces) and performing Boolean operations on the generated CAD modules. We address the complex reconstruction problem in four main steps. Firstly, we use a plane to cut the input mesh model and attain a loop cutting line, ensuring accurate normals. Secondly, the cutting line is automatically fitted to edges using primitive information and connected to form a primitive loop. This eliminates the need for time-consuming manual selection of each endpoint and significantly accelerates the reconstruction process. Subsequently, we construct the loop of primitives as a chunked CAD model through a series of CAD procedural operations, including extruding, lofting, revolving, and sweeping. Our approach incorporates an automatic height detection mechanism to minimize errors that may arise from manual designation of the extrusion height. Finally, by merging Boolean operations, these CAD models are assembled together to closely approximate the target geometry. We conduct a comprehensive evaluation of our algorithm using a diverse range of CAD models from both the Thingi10K dataset and real- world scans. The results validate that our method consistently delivers accurate, efficient, and robust reconstruction outcomes while minimizing the need for manual interactions. Furthermore, our approach demonstrates superior performance compared to competing methods, especially when applied to intricate geometries.
Publication:
Computer Aided Geometric Design Volume 111, June 2024
https://doi.org/10.1016/j.cagd.2024.102339
Author:
Zhenyu Zhang
MAIS, Institute of Automation, Chinese Academy of Sciences, China
KLMM, AMSS, Chinese Academy of Sciences, China
University of Chinese Academy of Sciences, China
Mingyang Zhao
Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, China
Zeyu Shen
Institute of Software, Chinese Academy of Sciences, China
University of Chinese Academy of Sciences, China
Yuqing Wang
KLMM, AMSS, Chinese Academy of Sciences, China
University of Chinese Academy of Sciences, China
Xiaohong Jia
KLMM, AMSS, Chinese Academy of Sciences, China
University of Chinese Academy of Sciences, China
Dong-Ming Yan
MAIS, Institute of Automation, Chinese Academy of Sciences, China
University of Chinese Academy of Sciences, China
MAIS, Institute of Automation, Chinese Academy of Sciences, China.
Email: yandongming@gmail.com
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