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

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

17 Jul 2024arXiv:2407.12702archive 2025-07-28

Elona Dupont, Kseniya Cherenkova, Dimitrios Mallis, Gleb Gusev, Anis Kacem, Djamila Aouada

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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Tasks

CAD Reconstruction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
CAD Reconstruction DeepCAD TransCAD Camfer Distance (median) 4.51 #8 of 11 Archive leaderboard report
CAD Reconstruction DeepCAD TransCAD Chamfer Distance 32.3 #8 of 11 Archive leaderboard report
CAD Reconstruction DeepCAD TransCAD IoU 65.5 #8 of 11 Archive leaderboard report
CAD Reconstruction Fusion 360 Gallery TransCAD Chamfer Distance 78.6 #8 of 11 Archive leaderboard report
CAD Reconstruction Fusion 360 Gallery TransCAD Chamfer Distance (median) 33.4 #8 of 11 Archive leaderboard report
CAD Reconstruction Fusion 360 Gallery TransCAD IoU 60.2 #8 of 11 Archive leaderboard report

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