TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
Elona Dupont*, Kseniya Cherenkova, Dimitrios Mallis, Gleb A Gusev, Anis Kacem, Djamila Aouada
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."
BibTeX
@inproceedings{eccv2024_transcadahierarc,
title = {TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds},
author = {Elona Dupont* and Kseniya Cherenkova and Dimitrios Mallis and Gleb A Gusev and Anis Kacem and Djamila Aouada},
booktitle = {ECCV 2024},
year = {2024}
}