1. Benchmarking CAD Search Techniques
Dmitriy Bespalov† Cheuk Yiu Ip†
Joshua Shaffer† William C. Regli†‡
Department of Computer Science†
Department of Mechanical Engineering & Mechanics‡
College of Engineering
Drexel University
3141 Chestnut Street
Philadelphia, PA 19104

2. Overview Introduction & Motivation Comparison techniques Datasets
Evaluation results
Discussion and conclusions

3. CAD vs Shape Representation
CAD Representation
Shape Representation
Topologically and geometrically consistent
Implicit and analytic surfaces, NURBS, etc
Produced using CAD packages and solid modelers
Approximate representation, error prone
Mesh or point cloud
Produced using animation tools, laser scanners

4. Motivation
Multiple shape and solid based retrieval techniques available today
Most researchers use their own datasets
How to measure performance of retrieval techniques against each other?
Need standard datasets for evaluating retrieval techniques on CAD/CAM artifacts

5. Techniques Used in Evaluation
Solid based comparison techniques
B-Rep based techniques
Invariant topological vector (ITV) [McWherter et al. 2001]
Eigenspace indexing on B-Rep graphs (Eigen-BRep) [Peabody 2002]
Feature based techniques
Model dependency graph approximate matching (MDG) [Cicirello and Regli 2002]
Eigenspace indexing on machining feature interaction graphs (Eigen-Feat) [Peabody 2002]

6. Techniques Used in Evaluation
Shape based comparison techniques
Shape distributions (SD) [Osada et al. 2002]
Shape distributions with point pair classifications (SD-Class) [Ip et al. 2002]
Shape distributions with weights learning (SD-Learn) [Ip et al. 2003]
Reeb graph comparison (Reeb) [Hilaga et al. 2001]
Scale-Space comparison (Scale-Space) [Bespalov et al. 2003]

7. Datasets Five datasets (ACIS SAT, STEP, VRML formats)
Synthetic Datasets
Primitive Dataset
Minor Topological Variation Dataset
Actual Artifacts Dataset
The National Design Repository Dataset
Functional Classification
Manufacturing Classification
LEGO© Dataset
Variable Fidelity Dataset (VRML only)

8. Primitive Dataset
Classification by type
Cubes
Cylinders
Spheres
Tori

9. Primitive Dataset
Classification by deformation
1 x 1 x 1
2 x 1 x 1 …

10. Primitive Dataset

11. Minor Topological Variation Dataset
Cubes
Bricks
1 hole
2 hole
3 holes
4 holes
0 holes
1 hole
2 holes
3 holes
4 holes

12. Minor Topological Variation Dataset

13. LEGO Dataset
X-Shape Axles
Cylindrical Parts
Wheels-Gears
Plates

14. LEGO Dataset

15. National Design Repository Dataset

16. Functional Classification
Springs
Screws
Gears
Nuts
Brackets
Housings
Linkage arms
Functional
Classification

17. Functional Classification

18. Manufacturing Classification
Cast-then-machined:
Prismatic Machined:

19. Manufacturing Classification

20. Variable Fidelity Dataset
Subset of National Design Repository Dataset
Functional Classification
Three Refinement Settings
Low Fidelity
High Fidelity
Normal Fidelity

21. Variable Fidelity Dataset

22. Evaluation Results Each technique was evaluated on every dataset
Evaluation Procedure:
Use k-nearest neighbor classification (kNN)
Compute recall and precision measures
Plot precision against recall graphs

23. Precision-Recall
Relevant models: number of models that fall in the same category as query model
Retrieved models: number of models returned by a query
Retrieved and Relevant models: number of models returned, that fell into the same category as query model

24. Minor Topological Variation Dataset: Bricks

25. Minor Topological Variation Dataset: Bricks

26. Minor Topological Variation Dataset: Cubes

27. Minor Topological Variation Dataset: Cubes

28. Primitive Dataset: Classification by Topology

29. Primitive Dataset: Classification by Topology

30. Primitive Dataset: Classification by Geometry

31. Primitive Dataset: Classification by Geometry

32. LEGO Dataset

33. LEGO Dataset

34. Functional Classification

35. Functional Classification

36. Manufacturing Classification

37. Manufacturing Classification

38. Shape Distributions Refinement

39. Shape Distributions With Point Classification Refinement

40. Shape Distributions With Weights Learning Refinement

41. Reeb Graph Refinement

42. Scale-Space Refinement

43. Discussion Boundary Representations are very useful Open Problems:
Manufacturing classifications
Functional classifications
Develop more feature-based techniques

44. Conclusions
Established datasets for evaluating retrieval techniques on CAD/CAM artifacts
Studied nine different 3D shape and solid model matching techniques:
In general poor performance on CAD objects

45. Q&A
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