TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
<|reference_start|>TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds: 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.<|reference_end|>
@article{dupont2024transcad:,
    title={TransCAD: A Hierarchical Transformer for CAD Sequence Inference from
  Point Clouds},
    author={Elona Dupont, Kseniya Cherenkova, Dimitrios Mallis, Gleb Gusev, Anis
  Kacem, Djamila Aouada},
    journal={arXiv preprint arXiv:2407.12702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12702},
    primaryClass={cs.CV cs.AI}
}