The effective organization of CAD data archives is central to PLM and consequently content based retrieval of 2D drawings and 3D models is often seen as a "holy grail" for the industry. Given this context, it is not surprising that the vision of a "Google for shape", which enables engineers to search databases of 3D models for components similar in shape to a query part, has motivated numerous researchers to investigate algorithms for computing geometric similarity. Measuring the effectiveness of the many approaches proposed has in turn lead to the creation of benchmark datasets against which researchers can compare the performance of their search engines. However to be useful the datasets used to measure the effectiveness of 3D retrieval algorithms must not only define a collection of models, but also provide a canonical specification of their relative similarity. Because the objective of shape retrieval algorithms is (typically) to retrieve groups of objects that humans perceive as "similar" these benchmark similarity relationships have (by definition) to be manually determined through inspection.
|Publication status||Published - 2009|
|Event||Proceedings of the International Conference on Product Lifecycle Management - Bath, UK|
Duration: 6 Jul 2009 → 8 Jul 2009
|Conference||Proceedings of the International Conference on Product Lifecycle Management|
|Period||6/07/09 → 8/07/09|
- product lifecycle management
- benchmark clusters
- design engineering
- computer aided design
- 3D search
- 3D content based retrieval
- CAD database
- CAD data management
- shape benchmark
Jagadeesan, P., Wenzel, J., Corney, J. R., Yan, X. T., Sherlock, A., Torres-Sanchez, C., & Regli, W. (2009). Validation of purdue engineering shape benchmark clusters by crowdsourcing. Paper presented at Proceedings of the International Conference on Product Lifecycle Management, Bath, UK, .