Effectiveness and efficiency of three-dimensional shape retrieval

Abstract

The effectiveness and efficiency of a content-based three-dimensional shape search system are investigated for supporting the re-use of engineering designs. Search effectiveness is characterized by precision and recall values from repeated search experiments. We extract four feature vectors from each three-dimensional shape and compare them for effectiveness. The performance of an R-tree-based index structure is also evaluated to characterize search efficiency. Search efficiency is evaluated by the ratio of the number of visited nodes in a search operation to the number of nodes in a database index. A multi-step refinement approach is proposed to improve search effectiveness. Based on the results of the experiments with a database of real models, the effectiveness of using multi-step refinement is predicted to be 51% higher than that of one-shot search using a single feature vector, although the difference becomes smaller when the number of retrieved shapes is larger. The R-tree index significantly improves the efficiency of our search system. Search efficiency decreases with the dimensionality of the data records and the capacity of database nodes. Based on our experiments with the synthetic database, the efficiency is predicted to be stable when the size of a database increases.

Keywords:

• CAD
• Design reuse
• Content-based search
• Multi-step search

Acknowledgements

Initial funding for this project came from the 21st Century Research and Technology Fund award from the state of Indiana. The authors acknowledge the support from the Innovation Realization Lab at Purdue University and the University Faculty Scholar Award from Purdue University. They thank Prof. Zygmunt Pizlo in Psychological Sciences, Purdue University, for his advice.