TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
Abstract
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 , an end-to-end transformer-based architecture that predicts the CAD sequence from a point cloud. 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 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.
Cite
Text
Dupont et al. "TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73030-6_2Markdown
[Dupont et al. "TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/dupont2024eccv-transcad/) doi:10.1007/978-3-031-73030-6_2BibTeX
@inproceedings{dupont2024eccv-transcad,
title = {{TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds}},
author = {Dupont, Elona and Cherenkova, Kseniya and Mallis, Dimitrios and Gusev, Gleb A and Kacem, Anis and Aouada, Djamila},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-73030-6_2},
url = {https://mlanthology.org/eccv/2024/dupont2024eccv-transcad/}
}