Abstract:

Creating 3D models with both manufacturability and editability based on traditional Computer-Aided Design (CAD) is a complex and time-consuming task. In recent years, deep learning technology has shown great potential in the automated generation of CAD models and has become a research hotspot. However, most CAD generation models fail to fully utilize the geometric and semantic information contained in input data such as point clouds, images, and sketches, making it difficult to accurately control the generation direction through flexible conditional inputs. To address this issue, the directional generation of CAD models can be achieved by exploring the representational capability of the latent space, adopting a denoising diffusion probabilistic model, and using such conditional input data as guidance. Specifically, a Transformer-based autoencoder was first constructed to encode CAD parameter command sequences into a latent space. Subsequently, a denoising diffusion probabilistic model was established within this space to generate CAD feature vectors by integrating conditional encoding information from point clouds, images, or sketches. Finally, the feature vectors were reconstructed into 3D CAD models via a decoder. Experimental results demonstrated that the generated CAD models exhibited reasonable structures, smooth surfaces, and distinct geometric features. Compared with existing methods, a superior balance was achieved among shape diversity, distribution similarity, and fidelity. Furthermore, the generation quality of CAD models was effectively enhanced when point clouds, images, or sketches were utilized as conditional inputs. The relevant code has been open-sourced and is available at https://github.com/Ziyou-maker/LDM4CAD.

Key words: CAD generation, parametric modeling, diffusion model, latent diffusion model, conditional generation