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.