Papers › Hierarchical Neural Coding for Controllable CAD Model Generation
Hierarchical Neural Coding for Controllable CAD Model Generation
Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne, Karl D. D. Willis, Yasutaka Furukawa
This paper presents a novel generative model for Computer Aided Design (CAD) that 1) represents high-level design concepts of a CAD model as a three-level hierarchical tree of neural codes, from global part arrangement down to local curve geometry; and 2) controls the generation or completion of CAD models by specifying the target design using a code tree. Concretely, a novel variant of a vector quantized VAE with "masked skip connection" extracts design variations as neural codebooks at three levels. Two-stage cascaded auto-regressive transformers learn to generate code trees from incomplete CAD models and then complete CAD models following the intended design. Extensive experiments demonstrate superior performance on conventional tasks such as random generation while enabling novel interaction capabilities on conditional generation tasks. The code is available at https://github.com/samxuxiang/hnc-cad.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| CAD Reconstruction | DeepCAD | HNC-CAD | Camfer Distance (median) | 8.64 | #9 of 11 | Archive leaderboard | report |
| CAD Reconstruction | DeepCAD | HNC-CAD | IoU | 65.3 | #9 of 11 | Archive leaderboard | report |
| CAD Reconstruction | Fusion 360 Gallery | HNC-CAD | Chamfer Distance (median) | 36.8 | #6 of 11 | Archive leaderboard | report |
| CAD Reconstruction | Fusion 360 Gallery | HNC-CAD | IoU | 63.5 | #6 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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