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CAD Model Reconstruction Method from Point Clouds for IoT 3D Perception
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Shuai ZHAO, Zhen XIA, Junliang CHEN, Bo CHENG, Chenyang DU
Journal of Beijing University of Posts and Telecommunications | 2025, 48(5) : 17 - 24
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Journal of Beijing University of Posts and Telecommunications | 2025, 48(5): 17-24
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CAD Model Reconstruction Method from Point Clouds for IoT 3D Perception
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Shuai ZHAO, Zhen XIA, Junliang CHEN, Bo CHENG, Chenyang DU
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  • State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
doi: 10.13190/j.jbupt.2025-073
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With the growing demand for understanding complex three dimensions (3D) environments in fields like Internet of things (IoT)-driven autonomous navigation and related applications, the automatic reconstruction of structured, editable computer-aided design (CAD) models from point cloud data has become a critical task. However, current research mainly focuses on reconstructing CAD models from CAD command sequences, sketches, and extrusion operations, and commonly faces challenges such as excessive reconstruction steps and strong platform dependence. To this end, a high-precision geometric and topological CAD reconstruction method is proposed, specifically for IoT 3D perception. First, the boundary representation of CAD is decomposed into parametric information of geometric primitives and their topological structure. Then, a primitive variational autoencoder (PVAE) and a topological variational autoencoder (TVAE) are designed to model their geometric features and topological relationships, respectively. Furthermore, PointNet++ is used to extract multi-scale local features from point cloud data and fuse them into global features. A topological decoder and primitive decoders are then used to predict topological tree sequences and primitive parameters, achieving high-precision CAD model reconstruction with clearer boundary details. To validate the method's effectiveness, metrics such as chamfer distance, edge chamfer distance, and structural consistency are used to evaluate its performance on two datasets. The experimental results show that the proposed model outperforms existing methods in terms of topological integrity and geometric reconstruction accuracy,enabling higher-precision CAD model reconstruction.

Internet of things three dimensions perception  /  point cloud  /  computer-aided design  /  boundary representation  /  PointNet++  /  variational autoencoder
Shuai ZHAO, Zhen XIA, Junliang CHEN, Bo CHENG, Chenyang DU. CAD Model Reconstruction Method from Point Clouds for IoT 3D Perception[J]. Journal of Beijing University of Posts and Telecommunications, 2025 , 48 (5) : 17 -24 . DOI: 10.13190/j.jbupt.2025-073
Year 2025 volume 48 Issue 5
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doi: 10.13190/j.jbupt.2025-073
  • Receive Date:2025-07-14
  • Online Date:2026-04-16
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  • Received:2025-07-14
Affiliations
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
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