CAD Model Retrieval Based on Graduated Assignment Algorithm
A retrieval approach for CAD models based on graduated assignment algorithm is proposed in this paper. First, CAD models are transformed into face adjacency graphs (FAGs). Second, the vertex compatibility matrix and edge compatibility matrix between the FAGs of the query and data models are calculated, and the similarity metric for the two comparison models is established from their compatibility matrices, which serves as the optimization objective function for selecting vertex mapping matrix M between the two comparison models. Finally, Sinkhorns alternative normalization approach for Ms rows and columns is adopted to find the optimal vertex mapping matrix M. Experimental results have shown that the proposed approach supports CAD model retrieval.
model retrieval graduated assignment algorithm face adjacency graph