1. Computer Vision : CISC 4/689 CREDITS Rasmussen, UBC (Jim Little), Seitz (U. of Wash.), Camps (Penn. State), UC, UMD (Jacobs), UNC, CUNY

2. Computer Vision : CISC 4/689 Multi-View Geometry Relates 3D World Points Camera Centers Camera Orientations

3. Computer Vision : CISC 4/689 Multi-View Geometry Relates 3D World Points Camera Centers Camera Orientations Camera Intrinsic Parameters Image Points

4. Computer Vision : CISC 4/689 Stereo scene point optical center image plane

5. Computer Vision : CISC 4/689 Stereo Basic Principle: Triangulation –Gives reconstruction as intersection of two rays –Requires calibration point correspondence

6. Computer Vision : CISC 4/689 Stereo Constraints p p’ ? Given p in left image, where can the corresponding point p’ in right image be?

7. Computer Vision : CISC 4/689 Stereo Constraints X1X1 Y1Y1 Z1Z1 O1O1 Image plane Focal plane M p p’ Y2Y2 X2X2 Z2Z2 O2O2 Epipolar Line Epipole

8. Computer Vision : CISC 4/689 Stereo The geometric information that relates two different viewpoints of the same scene is entirely contained in a mathematical construct known as fundamental matrix. The geometry of two different images of the same scene is called the epipolar geometry.

9. Computer Vision : CISC 4/689 Stereo/Two-View Geometry The relationship of two views of a scene taken from different camera positions to one another Interpretations –“Stereo vision” generally means two synchronized cameras or eyes capturing images –Could also be two sequential views from the same camera in motion Assuming a static scene http://www-sop.inria.fr/robotvis/personnel/sbougnou/Meta3DViewer/EpipolarGeo

10. Computer Vision : CISC 4/689 3D from two-views There are two ways of extracting 3D from a pair of images. Classical method, called Calibrated route, we need to calibrate both cameras (or viewpoints) w.r.t some world coordinate system. i.e, calculate the so-called epipolar geometry by extracting the essential matrix of the system. Second method, called uncalibrated route, a quantity known as fundamental matrix is calculated from image correspondences, and this is then used to determine the 3D. Either way, principle of binocular vision is triangulation. Given a single image, the 3D location of any visible object point must lie on the straight line that passes through COP and image point (see fig.). Intersection of two such lines from two views is triangulation.

11. Computer Vision : CISC 4/689 Mapping Points between Images What is the relationship between the images x, x’ of the scene point X in two views? Intuitively, it depends on: –The rigid transformation between cameras (derivable from the camera matrices P, P’ ) –The scene structure (i.e., the depth of X ) Parallax: Closer points appear to move more

12. Computer Vision : CISC 4/689 Example: Two-View Geometry courtesy of F. Dellaert x1x1 x’1x’1 x2x2 x’2x’2 x3x3 x’3x’3 Is there a transformation relating the points x i to x’ i ?

13. Computer Vision : CISC 4/689 Epipolar Geometry Baseline: Line joining camera centers C, C’ Epipolar plane ¦ : Defined by baseline and scene point X from Hartley & Zisserman baseline

14. Computer Vision : CISC 4/689 Epipolar Lines Epipolar lines l, l’ : Intersection of epipolar plane ¦ with image planes Epipoles e, e’ : Where baseline intersects image planes –Equivalently, the image in one view of the other camera center. C C’C’ from Hartley & Zisserman

15. Computer Vision : CISC 4/689 Epipolar Pencil As position of X varies, epipolar planes “rotate” about the baseline (like a book with pages) –This set of planes is called the epipolar pencil Epipolar lines “radiate” from epipole—this is the pencil of epipolar lines from Hartley & Zisserman

16. Computer Vision : CISC 4/689 Epipolar Constraint Camera center C and image point define ray in 3-D space that projects to epipolar line l’ in other view (since it’s on the epipolar plane) 3-D point X is on this ray, so image of X in other view x’ must be on l’ In other words, the epipolar geometry defines a mapping x ! l’, of points in one image to lines in the other from Hartley & Zisserman C C’C’ x’x’

17. Computer Vision : CISC 4/689 Example: Epipolar Lines for Converging Cameras from Hartley & Zisserman Left viewRight view Intersection of epipolar lines = Epipole ! Indicates direction of other camera

18. Computer Vision : CISC 4/689 Special Case: Translation Parallel to Image Plane Note that epipolar lines are parallel and corresponding points lie on correspond- ing epipolar lines (the latter is true for all kinds of camera motions)

19. Computer Vision : CISC 4/689 From Geometry to Algebra O O’ P p p’

20. Computer Vision : CISC 4/689 From Geometry to Algebra O O’ P p p’

21. Computer Vision : CISC 4/689 Linear Constraint: Should be able to express as matrix multiplication. Rotation arrow should be at the other end, to get p’ in p coordinates

22. Computer Vision : CISC 4/689 Review: Matrix Form of Cross Product

23. Computer Vision : CISC 4/689 Review: Matrix Form of Cross Product

24. Computer Vision : CISC 4/689 Matrix Form

25. Computer Vision : CISC 4/689 The Essential Matrix If un-calibrated, p gets multiplied by Intrisic matrix, K

26. Computer Vision : CISC 4/689 The Fundamental Matrix F Mapping of point in one image to epipolar line in other image x ! l’ is expressed algebraically by the fundamental matrix F Write this as l’ = F x Since x’ is on l’, by the point-on-line definition we know that x’ T l’ = 0 Substitute l’ = F x, we can thus relate corresponding points in the camera pair (P, P’) to each other with the following: x’ T F x = 0 line point

27. Computer Vision : CISC 4/689 The Fundamental Matrix F F is 3 x 3, rank 2 (not invertible, in contrast to homographies) –7 DOF (homogeneity and rank constraint take away 2 DOF) The fundamental matrix of (P’, P) is the transpose F T from Hartley & Zisserman x’x’ NOW, can get implicit equation for any x, which is epipolar line)

28. Computer Vision : CISC 4/689 Computing Fundamental Matrix Fundamental Matrix is singular with rank 2 In principal F has 7 parameters up to scale and can be estimated from 7 point correspondences Direct Simpler Method requires 8 correspondences (u’ is same as x in the prev. slide, u’ is same as x)

29. Computer Vision : CISC 4/689 Estimating Fundamental Matrix Each point correspondence can be expressed as a linear equation The 8-point algorithm

30. Computer Vision : CISC 4/689 The 8-point Algorithm Lot of squares, so numbers have varied range, from say 1000 to 1. So pre-normalize. And RANSaC!

31. Computer Vision : CISC 4/689 Computing F: The Eight-point Algorithm Input: n point correspondences ( n >= 8) –Construct homogeneous system Ax= 0 from x = (f 11,f 12,,f 13, f 21,f 22,f 23 f 31,f 32, f 33 ) : entries in F Each correspondence gives one equation A is a nx9 matrix (in homogenous format) –Obtain estimate F^ by SVD of A x (up to a scale) is column of V corresponding to the least singular value –Enforce singularity constraint: since Rank (F) = 2 Compute SVD of F^ Set the smallest singular value to 0: D -> D’ Correct estimate of F : Output: the estimate of the fundamental matrix, F’ Similarly we can compute E given intrinsic parameters

32. Computer Vision : CISC 4/689 Locating the Epipoles from F Input: Fundamental Matrix F –Find the SVD of F –The epipole e l is the column of V corresponding to the null singular value (as shown above) –The epipole e r is the column of U corresponding to the null singular value Output: Epipole e l and e r e l lies on all the epipolar lines of the left image F is not identically zero True For every p r p l p r P OlOl OrOr elel erer PlPl PrPr Epipolar Plane Epipolar Lines Epipoles

33. Computer Vision : CISC 4/689 Special Case: Translation along Optical Axis Epipoles coincide at focus of expansion Not the same (in general) as vanishing point of scene lines from Hartley & Zisserman

34. Computer Vision : CISC 4/689 Finding Correspondences Epipolar geometry limits where feature in one image can be in the other image –Only have to search along a line

35. Computer Vision : CISC 4/689 Simplest Case Image planes of cameras are parallel. Focal points are at same height. Focal lengths same. Then, epipolar lines are horizontal scan lines.

36. Computer Vision : CISC 4/689 We can always achieve this geometry with image rectification Image Reprojection –reproject image planes onto common plane parallel to line between optical centers Notice, only focal point of camera really matters (Seitz)

37. Computer Vision : CISC 4/689 Stereo Rectification Rectification –Given a stereo pair, the intrinsic and extrinsic parameters, find the image transformation to achieve a stereo system of horizontal epipolar lines –A simple algorithm: Assuming calibrated stereo cameras p’ l r P OlOl OrOr X’ r PlPl PrPr Z’ l Y’ l Y’ r TX’ l Z’ r n Stereo System with Parallel Optical Axes n Epipoles are at infinity n Horizontal epipolar lines

38. Computer Vision : CISC 4/689 Stereo Rectification Algorithm –Rotate both left and right camera so that they share the same X axis : O r -O l = T –Define a rotation matrix R rect for the left camera –Rotation Matrix for the right camera is R rect R T –Rotation can be implemented by image transformation p l p r P OlOl OrOr XlXl XrXr PlPl PrPr ZlZl YlYl ZrZr YrYr R, T TX’ l X l ’ = T_axis, Y l ’ = X l ’xZ l, Z’ l = X l ’xY l ’

39. Computer Vision : CISC 4/689 Stereo Rectification Algorithm –Rotate both left and right camera so that they share the same X axis : O r -O l = T –Define a rotation matrix R rect for the left camera –Rotation Matrix for the right camera is R rect R T –Rotation can be implemented by image transformation p l p r P OlOl OrOr XlXl XrXr PlPl PrPr ZlZl YlYl ZrZr YrYr R, T TX’ l X l ’ = T_axis, Y l ’ = X l ’xZ l, Z’ l = X l ’xY l ’

40. Computer Vision : CISC 4/689 Stereo Rectification Algorithm –Rotate both left and right camera so that they share the same X axis : O r -O l = T –Define a rotation matrix R rect for the left camera –Rotation Matrix for the right camera is R rect R T –Rotation can be implemented by image transformation ZrZr p’ l r P OlOl OrOr X’ r PlPl PrPr Z’ l Y’ l Y’ r R, T TX’ l T’ = (B, 0, 0),

41. Computer Vision : CISC 4/689 Public Library, Stereoscopic Looking Room, Chicago, by Phillips, 1923

42. Computer Vision : CISC 4/689 Teesta suspension bridge-Darjeeling, India

43. Computer Vision : CISC 4/689 Mark Twain at Pool Table", no date, UCR Museum of Photography

44. Computer Vision : CISC 4/689 Woman getting eye exam during immigration procedure at Ellis Island, c. 1905 - 1920, UCR Museum of Phography

45. Computer Vision : CISC 4/689 Stereo matching attempt to match every pixel use additional constraints

46. Computer Vision : CISC 4/689 A Simple Stereo System Z w =0 LEFT CAMERA Left image: reference Right image: target RIGHT CAMERA Elevation Z w disparity Depth Z baseline

47. Computer Vision : CISC 4/689 Let’s discuss reconstruction with this geometry before correspondence, because it’s much easier. OlOlOlOl OrOrOrOr P plplplpl prprprpr T Z xlxlxlxl xrxrxrxr f T is the stereo baseline d measures the difference in retinal position between corresponding points (Camps) Then given Z, we can compute X and Y. Disparity: xl,yl=(f X/Z, f Y/Z) Xr,yr=(f (X-T)/Z, f Y/Z) d=xl-xr=f X/Z – f (X-T)/Z (  -ve,  +ve, refer previous slide fig.)

48. Computer Vision : CISC 4/689 Correspondence: What should we match? Objects? Edges? Pixels? Collections of pixels?

49. Computer Vision : CISC 4/689 Extracting Structure The key aspect of epipolar geometry is its linear constraint on where a point in one image can be in the other By correlation-matching pixels (or features) along epipolar lines and measuring the disparity between them, we can construct a depth map (scene point depth is inversely proportional to disparity) View 1View 2Computed depth map courtesy of P. Debevec

50. Computer Vision : CISC 4/689 Correspondence: Photometric constraint Same world point has same intensity in both images. –Lambertian fronto-parallel –Issues: Noise Specularity Foreshortening

51. Computer Vision : CISC 4/689 Using these constraints we can use matching for stereo For each epipolar line For each pixel in the left image compare with every pixel on same epipolar line in right image pick pixel with minimum match cost This will never work, so: Improvement: match windows (Seitz)

52. Computer Vision : CISC 4/689 Aggregation Use more than one pixel Assume neighbors have similar disparities * –Use correlation window containing pixel –Allows to use SSD, ZNCC, etc.

53. Computer Vision : CISC 4/689 Comparing Windows: =?f g Mostpopular (Camps) For each window, match to closest window on epipolar line in other image.

54. Computer Vision : CISC 4/689 Compare intensities pixel-by-pixel Comparing image regions I(x,y) I´(x,y) Sum of Square Differences Dissimilarity measures

55. Computer Vision : CISC 4/689 Compare intensities pixel-by-pixel Comparing image regions I(x,y) I´(x,y) Zero-mean Normalized Cross Correlation Similarity measures

56. Computer Vision : CISC 4/689 Aggregation window sizes Small windows disparities similar more ambiguities accurate when correct Large windows larger disp. variation more discriminant often more robust use shiftable windows to deal with discontinuities (Illustration from Pascal Fua)

57. Computer Vision : CISC 4/689 Window size W = 3W = 20 Better results with adaptive window T. Kanade and M. Okutomi, A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment,, Proc. International Conference on Robotics and Automation, 1991.A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment D. Scharstein and R. Szeliski. Stereo matching with nonlinear diffusion. International Journal of Computer Vision, 28(2):155-174, July 1998Stereo matching with nonlinear diffusion Effect of window size (Seitz)

58. Computer Vision : CISC 4/689 Correspondence Using Window- based matching LeftRight SSD error disparity Left Right scanline

59. Computer Vision : CISC 4/689 Sum of Squared (Pixel) Differences LeftRight

60. Computer Vision : CISC 4/689 Image Normalization Even when the cameras are identical models, there can be differences in gain and sensitivity. The cameras do not see exactly the same surfaces, so their overall light levels can differ. For these reasons and more, it is a good idea to normalize the pixels in each window:

61. Computer Vision : CISC 4/689 Stereo results Ground truthScene –Data from University of Tsukuba (Seitz)

62. Computer Vision : CISC 4/689 Results with window correlation Window-based matching (best window size) Ground truth (Seitz)

63. Computer Vision : CISC 4/689 Results with better method State of the art method Boykov et al., Fast Approximate Energy Minimization via Graph Cuts,Fast Approximate Energy Minimization via Graph Cuts International Conference on Computer Vision, September 1999. Ground truth (Seitz)