arXiv Computer Vision
Sep 11

Language-Augmented Semantic Priors for B-Spline Surface Fitting

The paper introduces LASP, a framework that uses large language models to generate structured B‑spline priors from procedural modeling histories. By translating design intent into rich textual descriptions, LASP provides semantic reasoning that guides conventional CAD solvers toward more accurate and coherent surface fitting. Experiments show that language‑driven priors outperform traditional machine learning approaches, establishing a new paradigm for language‑guided geometric optimization.

By Yunzhong Lou, Yusheng Luo, Jiahao Li, Yu Song, Xiangdong Zhou
arXiv AI
Jul 1

Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling

arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.

By Fumin Liu, Haoyu Zhou, Fei Hao, Lin Yang
arXiv Machine Learning
Sep 2

CADKnitter: Compositional CAD Generation from Text and Geometry Guidance

CADKnitter is a compositional CAD generation framework that uses geometric-guiding cues to steer diffusion sampling, enabling the creation of complementary CAD parts that satisfy both geometric constraints of an existing model and semantic constraints from a text prompt. The authors introduce KnitCAD, a dataset of over 310,000 CAD models paired with textual prompts and assembly metadata to support training and evaluation. Experiments show that CADKnitter outperforms state‑of‑the‑art baselines by a clear margin.

By Tri Le, Khang Nguyen, Baoru Huang, Tung D. Ta, Anh Nguyen