Contrastive Learning to Align Drawings and CAD Models for Retrieval

Retrieving computer-aided design (CAD) models from drawings can support the discovery and reuse of existing designs in engineering workflows. However, differences between visual depictions and parametric model representations make it challenging to identify the relevant CAD model, particularly when the query image has been altered. This paper proposes a contrastive learning framework that aligns CAD drawings with CAD model representations in a shared embedding space. The framework learns correspondences between drawings and model construction sequences, enabling retrieval through embedding similarity. Beyond matching an original drawing to its corresponding CAD model, the approach also retrieves relevant models from altered query images. We evaluate the framework on a dataset of parametric CAD command sequences derived from the ABC dataset, assessing retrieval performance under both original and altered query conditions. Experimental results show improved retrieval performance compared with baseline methods, demonstrating that contrastive learning can effectively align CAD drawings with parametric CAD model representations. These findings support the applicability of contrastive learning beyond image–text alignment to image–CAD alignment, enabling CAD model retrieval directly from visual queries.