Parametric CAD Primitive Retrieval via Multi-Modal Fusion and Deep Hashing

作者：Minyang Xu, Yunzhong Lou, Weijian Ma, Xueyang Li, Xiangdong Zhou · 年份：2024 · DOI：10.1145/3652583.3658041 · 被引用次数：2 · 研究领域：Advanced Image and Video Retrieval Techniques、Image Processing and 3D Reconstruction、3D Shape Modeling and Analysis

In the rapidly evolving field of manufacturing industry, product designers need to be able to quickly and accurately retrieve and reference existing Computer-Aided Design (CAD) primitives to increase efficiency and foster innovation. This paper introduces an innovative deep hashing-based parametric CAD primitive retrieval model DH-CAD, which employs a deep learning framework to capture the profound multi-modal features of parametric CAD primitive (command sequences and point clouds), and utilizes hashing to effectively encode these features into compact binary hash codes, significantly improving retrieval efficiency and accuracy. DH-CAD optimizes the original sequence encodings of Transformer to effectively learn the intricate relationships between CAD tool's command sequences and the corresponding point clouds of CAD primitive. Furthermore, it considers the dependencies between command types and parameter values, combines the output of the command type decoder with the prediction of parameter values to further refine the generated parameters, and employs a binarization network to generate hash codes in an end-to-end manner. Experimental results clearly demonstrate that, compared to traditional feature-matching methods, DH-CAD achieves the state-of-the-art performance on multiple evaluation metrics. Particularly in terms of retrieval speed and accuracy, our proposed model not only retrieves sequences that are most relevant to the query rapidly, but also ensures the high preci...