TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913435039563776 |
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| author | Dupont, Elona Cherenkova, Kseniya Mallis, Dimitrios Gusev, Gleb Kacem, Anis Aouada, Djamila |
| author_facet | Dupont, Elona Cherenkova, Kseniya Mallis, Dimitrios Gusev, Gleb Kacem, Anis Aouada, Djamila |
| contents | 3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a research direction that offers many promising practical applications. This paper proposes TransCAD, an end-to-end transformer-based architecture that predicts the CAD sequence from a point cloud. TransCAD leverages the structure of CAD sequences by using a hierarchical learning strategy. A loop refiner is also introduced to regress sketch primitive parameters. Rigorous experimentation on the DeepCAD and Fusion360 datasets show that TransCAD achieves state-of-the-art results. The result analysis is supported with a proposed metric for CAD sequence, the mean Average Precision of CAD Sequence, that addresses the limitations of existing metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds Dupont, Elona Cherenkova, Kseniya Mallis, Dimitrios Gusev, Gleb Kacem, Anis Aouada, Djamila Computer Vision and Pattern Recognition Artificial Intelligence 3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a research direction that offers many promising practical applications. This paper proposes TransCAD, an end-to-end transformer-based architecture that predicts the CAD sequence from a point cloud. TransCAD leverages the structure of CAD sequences by using a hierarchical learning strategy. A loop refiner is also introduced to regress sketch primitive parameters. Rigorous experimentation on the DeepCAD and Fusion360 datasets show that TransCAD achieves state-of-the-art results. The result analysis is supported with a proposed metric for CAD sequence, the mean Average Precision of CAD Sequence, that addresses the limitations of existing metrics. |
| title | TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2407.12702 |