BRepFormer: Transformer-Based B-rep Geometric Feature Recognition
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913912724652032 |
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| author | Dai, Yongkang Huang, Xiaoshui Bai, Yunpeng Guo, Hao Gan, Hongping Yang, Ling Shi, Yilei |
| author_facet | Dai, Yongkang Huang, Xiaoshui Bai, Yunpeng Guo, Hao Gan, Hongping Yang, Ling Shi, Yilei |
| contents | Recognizing geometric features on B-rep models is a cornerstone technique for multimedia content-based retrieval and has been widely applied in intelligent manufacturing. However, previous research often merely focused on Machining Feature Recognition (MFR), falling short in effectively capturing the intricate topological and geometric characteristics of complex geometry features. In this paper, we propose BRepFormer, a novel transformer-based model to recognize both machining feature and complex CAD models' features. BRepFormer encodes and fuses the geometric and topological features of the models. Afterwards, BRepFormer utilizes a transformer architecture for feature propagation and a recognition head to identify geometry features. During each iteration of the transformer, we incorporate a bias that combines edge features and topology features to reinforce geometric constraints on each face. In addition, we also proposed a dataset named Complex B-rep Feature Dataset (CBF), comprising 20,000 B-rep models. By covering more complex B-rep models, it is better aligned with industrial applications. The experimental results demonstrate that BRepFormer achieves state-of-the-art accuracy on the MFInstSeg, MFTRCAD, and our CBF datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BRepFormer: Transformer-Based B-rep Geometric Feature Recognition Dai, Yongkang Huang, Xiaoshui Bai, Yunpeng Guo, Hao Gan, Hongping Yang, Ling Shi, Yilei Computer Vision and Pattern Recognition Recognizing geometric features on B-rep models is a cornerstone technique for multimedia content-based retrieval and has been widely applied in intelligent manufacturing. However, previous research often merely focused on Machining Feature Recognition (MFR), falling short in effectively capturing the intricate topological and geometric characteristics of complex geometry features. In this paper, we propose BRepFormer, a novel transformer-based model to recognize both machining feature and complex CAD models' features. BRepFormer encodes and fuses the geometric and topological features of the models. Afterwards, BRepFormer utilizes a transformer architecture for feature propagation and a recognition head to identify geometry features. During each iteration of the transformer, we incorporate a bias that combines edge features and topology features to reinforce geometric constraints on each face. In addition, we also proposed a dataset named Complex B-rep Feature Dataset (CBF), comprising 20,000 B-rep models. By covering more complex B-rep models, it is better aligned with industrial applications. The experimental results demonstrate that BRepFormer achieves state-of-the-art accuracy on the MFInstSeg, MFTRCAD, and our CBF datasets. |
| title | BRepFormer: Transformer-Based B-rep Geometric Feature Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.07378 |