TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds

• URL: http://arxiv.org/abs/2407.12702v2
• Date: Thu, 18 Jul 2024 10:27:36 GMT
• Title: TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
• Authors: Elona Dupont, Kseniya Cherenkova, Dimitrios Mallis, Gleb Gusev, Anis Kacem, Djamila Aouada,
• Abstract summary: 3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a promising research direction. This paper proposes TransCAD, an end-to-end transformer-based architecture that predicts the CAD sequence from a point cloud.
• Score: 14.631669857987271
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

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