ChatCAD: An MLLM-Guided Framework for Zero-shot CAD Drawing Restoration

Jing Tang, Hongru Xiao, Xiang Li, Wei Wang, Zeyu Gong

Abstract: CAD drawing restoration is one of the most urgent needs in industrial manufacturing. The existing research focuses on the digitization of CAD drawings, However, there are actually many problems in digitized CAD drawings due to the upgrading of engineering drafting software, and it is difficult to repair reliably. In this paper, we use multi-modal large language models (MLLMs) to carry out digital CAD drawing restoration, and we use Retrieval-Augmented Generation (RAG) technology to inject engineering domain knowledge into MLLMs. In addition, a complete set of multiagent systems is constructed to realize restoration in accordance with the CAD drawing review process in the mechanical field. We collected 1,639 CAD drawings of bearing seats to evaluate our multi-agent system, verifying its reliability and robustness to various problematic drawings. Notably, ChatCAD also has a much simpler implementation than alternative methods trained on a huge dataset.

External IDs:dblp:conf/icassp/TangXLWG25