1 / 32

A Flexible Camera Calibration Tool for 3D Capture

A Flexible Camera Calibration Tool for 3D Capture. Lei Wang Media and Machine Lab Advisor: Cindy Grimm. 3D Capture. Data Acquisition Camera Calibration Shape Integration Texture Synthesis Shape Texture Integration. Camera Calibration. Camera Model Global Approach Requirement

niveditha
Download Presentation

A Flexible Camera Calibration Tool for 3D Capture

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A Flexible Camera Calibration Tool for 3D Capture Lei Wang Media and Machine Lab Advisor: Cindy Grimm

  2. 3D Capture • Data Acquisition • Camera Calibration • Shape Integration • Texture Synthesis • Shape Texture Integration

  3. Camera Calibration • Camera Model • Global Approach • Requirement • Previous Work • Our Approach • Conclusion

  4. Pinhole Camera Model

  5. Denotation • 2D Point: m = [u, v, 1]T • 3D Point: M = [X, Y, Z]T • 2D – 3D: s m =A [R T] M • A: Intrinsic Parameters • [R T]: Extrinsic Parameters

  6. Equation

  7. Global Approach • Take pictures • Detect the pattern • Extract the feature • Solve for intrinsic parameters (one for all) • Solve for extrinsic parameters (one for each)

  8. Requirement • Automated • Hemisphere Visibility • Not occlude the object • Robust on lighting conditions • Easy to build • Easy to detect

  9. Planar Pattern • Checkerboard Tsai’s Algorithm Zhang’s Algorithm • Geometry Pattern • Concentric Circle Jun-Sik Kim, Ho-Won Kim and In-So Kweon’s Algorithm

  10. Improved Planar Pattern • Move the pattern by hand • Place two or more patterns on different plane • Place mirrors and use the additional reflected pattern

  11. Our Approach – Outline • Design Calibrating Pattern • Build Calibrating Pattern • Feature Extraction • Feature Mapping – Gradient Search • Test

  12. Cone with basic 3D shapes • Automated • Hemisphere visibility • Not occlude the object: part of the shape is enough for relying on a group of points • Robust for lighting conditions: color ratio • Easy to detect: color boundary • Easy to build: build from a planar pattern

  13. Basic Shapes • Circles • Ellipses • Lines • Points

  14. Stable Color Ratio

  15. Parameterized Representation • Line • Circle • Ellipse

  16. The Printable Planar Pattern • 3D equation, 2D print translation • Physical tips

  17. Boundary Detection • Color ratio • Grouping

  18. Ellipses • Ellipse detection • Ellipse fitting

  19. Lines • Line detection • Line fitting

  20. Points • Direct Point detection • Intersection of two shapes - Line-line intersection - Line-circle intersection - Line-ellipse intersection - Ellipse-ellipse intersection(not recommend)

  21. Solve for Intrinsic Parameters • Checkerboard • OpenCV • Flagged Checkerboard

  22. Extrinsic by Points • Use Points • Linear • Limitation - Hard to label - Inaccurate - Six points rules not guaranteed

  23. Extrinsic by Shapes • Other Shape • Non-Linear • Gradient-decent Search - Cost Function - Initial Guess - Step Choose - Stop Condition

  24. Extrinsic by Lines • Cost Function • Initial Guess • Step Choose • Stop Condition

  25. Extrinsic by Conics • Cost Function • Initial Guess • Step Choose • Stop Condition

  26. General Extrinsic • General Case • Use Combination Pattern

  27. Test

  28. Conclusion • Easiness • Efficiency

  29. Conclusion • Accuracy

  30. References

  31. Questions?

  32. Thank You

More Related