MultiCAD: Contrastive Representation Learning for Multi-modal 3D Computer-Aided Design Models
Weijian Ma, Minyang Xu, Xueyang Li, Xiangdong Zhou
Abstract
CAD models are multimodal data where information and knowledge contained in construction sequences and shapes are complementary to each other and representation learning methods should consider both of them. Such traits have been neglected in previous methods learning unimodal representations. To leverage the information from both modalities, we develop a multimodal contrastive learning strategy where features from different modalities interact via contrastive learning paradigm, driven by a novel multimodal contrastive loss. Two pretext tasks on both geometry and sequence domains are designed along with a two-stage training strategy to make the representation focus on encoding geometric details and decoding representations into construction sequences, thus being more applicable to downstream tasks such as multimodal retrieval and CAD sequence reconstruction. Experimental results show that the performance of our multimodal representation learning scheme has surpassed the baselines and unimodal methods significantly.