Implicit relevance inference for assembly CAD model retrieval based on design correlation representation

Abstract

Assembly retrieval is a crucial technology for leveraging the extensive design knowledge embedded in CAD product instances. Current methods predominantly employ pairwise similarity measurements, which treat each product model as an isolated entity and overlook the intricate design correlations that reveal high-level design development relationships. To enhance the comprehension of product design correlations within retrieval systems, this paper introduces a novel method for implicit relevance inference in assembly retrieval based on design correlation. We define a part co-occurring relationship to capture the design correlations among assemblies by clustering parts based on shape similarity. At a higher level, all assemblies in the database are constructed as a multiple correlation network based on hypergraph, where the hyperedges represent the part co-occurring relationships. For a given query assembly, the implicit relevance between the query and other assemblies can be calculated by network structure inference. The problem is solved by using a random walk algorithm on the assembly hypergraph network. Comprehensive experiments have shown the effectiveness of the proposed assembly retrieval approach. The proposed method can be seen as an extension of existing pairwise similarity retrieval by further considering assembly relevance, which shows it has versatility and can enhance the effectiveness of existing pairwise similarity retrieval methods.