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Camera calibration

Camera calibration. Digital Visual Effects, Spring 2008 Yung-Yu Chuang 2008/4/15. with slides by Richard Szeliski, Steve Seitz, and Marc Pollefyes. Announcements. Project #2 is due midnight next Monday Results for project #1 artifacts voting. Project #1 artifact voting. Total 141 votes

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Camera calibration

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  1. Camera calibration Digital Visual Effects, Spring 2008 Yung-Yu Chuang 2008/4/15 with slides by Richard Szeliski, Steve Seitz, and Marc Pollefyes

  2. Announcements • Project #2 is due midnight next Monday • Results for project #1 artifacts voting

  3. Project #1 artifact voting • Total 141 votes • 33 of 37 artifacts got votes

  4. Honorable mention(8): 胡傳甡 高紹航

  5. Honorable mention(8): 劉俊良

  6. Honorable mention(10): 周伯相

  7. Third place (13): 羅聖傑 鄭京恆

  8. Second place (14): 葉蓉蓉 劉冠廷

  9. First place (17): 梁彧 吳孟松

  10. Outline • Camera projection models • Camera calibration • Nonlinear least square methods

  11. Camera projection models

  12. Pinhole camera

  13. Pinhole camera model (X,Y,Z) P origin p (x,y) principal point (optical center)

  14. Pinhole camera model principal point

  15. Pinhole camera model principal point

  16. Principal point offset principal point intrinsic matrix only related to camera projection

  17. Intrinsic matrix • non-square pixels (digital video) • skew • radial distortion Is this form of K good enough?

  18. Distortion • Radial distortion of the image • Caused by imperfect lenses • Deviations are most noticeable for rays that pass through the edge of the lens No distortion Pin cushion Barrel

  19. Camera rotation and translation extrinsic matrix

  20. Two kinds of parameters • internal or intrinsic parameters such as focal length, optical center, aspect ratio:what kind of camera? • external or extrinsic (pose) parameters including rotation and translation:where is the camera?

  21. Other projection models

  22. Orthographic projection • Special case of perspective projection • Distance from the COP to the PP is infinite • Also called “parallel projection”: (x, y, z) → (x, y) Image World

  23. Other types of projections • Scaled orthographic • Also called “weak perspective” • Affine projection • Also called “paraperspective”

  24. Illusion

  25. Illusion

  26. Fun with perspective

  27. Perspective cues

  28. Perspective cues

  29. Fun with perspective Ames room Ames video BBC story

  30. Forced perspective in LOTR

  31. Camera calibration

  32. Camera calibration • Estimate both intrinsic and extrinsic parameters • Mainly, two categories: • Photometric calibration: uses reference objects with known geometry • Self calibration: only assumes static scene, e.g. structure from motion

  33. Camera calibration approaches • linear regression (least squares) • nonlinear optimization

  34. Chromaglyphs (HP research)

  35. Multi-plane calibration Images courtesy Jean-Yves Bouguet, Intel Corp. • Advantage • Only requires a plane • Don’t have to know positions/orientations • Good code available online! • Intel’s OpenCV library:http://www.intel.com/research/mrl/research/opencv/ • Matlab version by Jean-Yves Bouget: http://www.vision.caltech.edu/bouguetj/calib_doc/index.html • Zhengyou Zhang’s web site: http://research.microsoft.com/~zhang/Calib/

  36. Step 1: data acquisition

  37. Step 2: specify corner order

  38. Step 3: corner extraction

  39. Step 3: corner extraction

  40. Step 4: minimize projection error

  41. Step 4: camera calibration

  42. Step 4: camera calibration

  43. Step 5: refinement

  44. Optimized parameters

  45. Camera calibration

  46. Linear regression

  47. Linear regression • Directly estimate 11 unknowns in the M matrix using known 3D points (Xi,Yi,Zi) and measured feature positions (ui,vi)

  48. Linear regression

  49. Linear regression

  50. Linear regression Solve for Projection Matrix M using least-square techniques

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