View-Based 3-D CAD Model Retrieval With Deep Residual Networks

Abstract

In industrial enterprises, effective retrieval and reuse of three-dimensional (3-D) computer-aided design (CAD) models could greatly save time and cost in new product development and manufacturing. Consequently, this article proposes a novel view-based approach for 3-D CAD model retrieval enabled by deep learning. This article constructs a multiview model dataset in industrial domain that collects solid and line views of database models. Since views contain rich information for differentiating these models, the problem of model retrieval is defined as a view recognition problem. Then, the extended deep residual networks (ResNets) are successfully trained to facilitate the model retrieval. With the learned networks, engineers could take a group of views, an engineering drawing, or even a hand-drawn sketch that represents their query intents as input and acquire the relevant 3-D CAD models and embedded knowledge for product lifecycle reuse. The experimental results demonstrate the effectiveness and efficiency of the approach.