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Technion

Israel Institute of Technology. Technion. Faculty of Electrical Engineering. Project A 044167. Summer 2001. 3D Geometric Objects Search. Project team: Lyakas Alexander 307666883 Instructor: Dr. Sigal Ar. • Given a collection (database) of objects.

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Technion

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  1. Israel Institute of Technology Technion Faculty of Electrical Engineering Project A 044167 Summer 2001

  2. 3D Geometric Objects Search • Project team: • Lyakas Alexander 307666883 • Instructor: Dr. Sigal Ar

  3. • Given a collection (database) of objects • Choose a search object • Find objects that are similar to the search object • The search is iterative and interactive • A user marks some objects as ‘GOOD’ or ‘BAD’ • The search program tries to refine the search by considering the user’s feedback The Main Idea

  4. The Main Idea – Cont.

  5. An Example

  6. An Example – Cont.

  7. Starting the Project • The following components available from Iterative and Interactive Search for Objects by Moty Golan & Oren Kerem based on Similarity Between Three-Dimensional Objects – An Iterative and Interactive Approach by Michael Elad, Ayellet Tal, Sigal Ar. • Two databases: 3D colorless models & 2D images • A search program

  8. Project Requirements • Test the approach with 3D colored models • Improve the search program • Design & perform system tests with real users

  9. Project Requirements - Cont. • Build a database of 3D colored models • Gather 3D colored objects from the WWW • Perform preprocessing calculations, i.e. present each object in a way that will enable searching

  10. Project Requirements - Cont. • Improve the search program • Adding a new database must not influence the search program’s code • Add features needed for testing

  11. Project Requirements - Cont. • Test the system with real users • Design the tests • Perform tests with volunteers • Draw conclusions

  12. • To calculate feature vectors we used moments of different orders on • colored points in 3D • colored normals in 3D • colorless points in 3D • colorless normals in 3D Working with Objects • Each object is presented as a numerical vector, AKA ‘feature vector’

  13. • Consider two objects represented as feature vectors: • We can compare them using the (square of) standard Euclidean distance: • By adding weights and a bias value we can refine the distance function: Comparing Objects

  14. Data Preprocessing • Convert the objects to the format convenient to be sampled • Perform sampling • Correct normals directions • Normalize rotation and scale • Create icons for all objects • Calculate features vectors

  15. • The sampling workflow: • Choose a triangle to sample Sampling • Before sampling each object is presented as colored triangular mesh Ensure uniform sampling • Sample a point, normal and color from the chosen triangle • Do this as many times as needed (10,000 in our case)

  16. • The pqr-th moment (of a 3D object) is defined as: • We approximate moments as: • Feature vector of level 3 in ‘colorless 3D’ look like: Calculating features • The order of the moment is p+q+r

  17. • The object-based solution introduces the DBLINK class • Database-specific information is stored inside DBLINK objects only • One DBLINK object for each database – stored on disk The Search Program • The extendibility requirement – adding new database must not influence the search program code

  18. The Search Program – Cont.

  19. • Automatic Screen Shooting • Before search refinements – with user’s ‘GOOD/BAD’ markings • When the new results are displayed Saving Test Sessions Results • Manual Screen Shooting

  20. Testing the system • Several volunteers that had no previous knowledge about how the system works • Tests were done for several test objects • For each test object – all search configurations were tried • The testers gave feedback on the search results

  21. Test Example 1

  22. Test Example 1 - Cont.

  23. Test Example 1 - Cont.

  24. Test Example 1 - Cont.

  25. Test Example 1 - Cont.

  26. Test Example 1 - Cont.

  27. Test Example 2 - Cont.

  28. Test Example 2 - Cont.

  29. Test Example 2 - Cont.

  30. Test Example 2 - Cont.

  31. Test Example 2 - Cont.

  32. Testing Results Example

  33. • In most cases the search converged • not always with good search results… • ‘Normals’, level 4 worked good • but slow… • So should the colors be considered? • … Conclusions • Searching for objects having a ‘family’ was successful with most configurations • No search configuration worked well for all objects

  34. The End • See the project book for many skipped details

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