KDH-CAD: Knowledge-data hybrid CAD learning under data scarcity
Deep learning in computer-aided design (CAD) remains fundamentally constrained by the data scarcity challenge: authentic CAD data is difficult to collect at scale, while synthetic data may not faithfully reflect real design practice. Rather than pursuing ever-larger CAD datasets, this paper alternatively treats CAD learning as a knowledge completion and calibration problem. It introduces KDH-CAD, a knowledge-data hybrid framework that integrates pretrained knowledge in foundation models, structured domain knowledge from textbooks/tutorials, and a very small amount of labeled CAD data. Domain knowledge is used to elicit and complete CAD-relevant concepts that are weakly expressed or under-...
| Authors | Ziqin Gao, Zhijie Yang, Qiang Zou |
|---|---|
| Primary category | cs.GR |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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