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Projective geometry

Projective geometry. Ames Room. Slides from Steve Seitz and Daniel DeMenthon. Projective geometry—what’s it good for?. Uses of projective geometry Drawing Measurements Mathematics for projection Undistorting images Focus of expansion Camera pose estimation, match move Object recognition.

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Projective geometry

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  1. Projective geometry Ames Room Slides from Steve Seitz and Daniel DeMenthon

  2. Projective geometry—what’s it good for? • Uses of projective geometry • Drawing • Measurements • Mathematics for projection • Undistorting images • Focus of expansion • Camera pose estimation, match move • Object recognition

  3. Applications of projective geometry Vermeer’s Music Lesson

  4. Measurements on planes 4 3 2 1 1 2 3 4 Approach: unwarp then measure What kind of warp is this?

  5. Image rectification p’ p • To unwarp (rectify) an image • solve for homography H given p and p’ • solve equations of the form: wp’ = Hp • linear in unknowns: w and coefficients of H • H is defined up to an arbitrary scale factor • how many points are necessary to solve for H?

  6. Solving for homographies

  7. Solving for homographies 2n × 9 9 2n • Linear least squares • Since h is only defined up to scale, solve for unit vector ĥ • Minimize A h 0 • Solution: ĥ = eigenvector of ATA with smallest eigenvalue • Works with 4 or more points

  8. The projective plane (x,y,1) image plane • Why do we need homogeneous coordinates? • represent points at infinity, homographies, perspective projection, multi-view relationships • What is the geometric intuition? • a point in the image is a ray in projective space y (sx,sy,s) (0,0,0) x z • Each point(x,y) on the plane is represented by a ray(sx,sy,s) • all points on the ray are equivalent: (x, y, 1)  (sx, sy, s)

  9. Projective lines • A line is a plane of rays through origin • all rays (x,y,z) satisfying: ax + by + cz = 0 l p • A line is also represented as a homogeneous 3-vector l • What does a line in the image correspond to in projective space?

  10. Point and line duality l1 p l l2 • A line l is a homogeneous 3-vector • It is  to every point (ray) p on the line: lp=0 p2 p1 • What is the line l spanned by rays p1 and p2 ? • l is  to p1 and p2  l = p1p2 • l is the plane normal • What is the intersection of two lines l1 and l2 ? • p is  to l1 and l2  p = l1l2 • Points and lines are dual in projective space • given any formula, can switch the meanings of points and lines to get another formula

  11. Ideal points and lines (a,b,0) y y (sx,sy,0) z z image plane image plane x x • Ideal line • l  (a, b, 0) – parallel to image plane • Ideal point (“point at infinity”) • p  (x, y, 0) – parallel to image plane • It has infinite image coordinates • Corresponds to a line in the image (finite coordinates)

  12. Homographies of points and lines • Computed by 3x3 matrix multiplication • To transform a point: p’ = Hp • To transform a line: lp=0  l’p’=0 • 0 = lp = lH-1Hp = lH-1p’  l’ = lH-1 • lines are transformed bypostmultiplication of H-1

  13. 3D to 2D: “perspective” projection • Matrix Projection: • What is not preserved under perspective projection? • What IS preserved?

  14. Vanishing points vanishing point • Vanishing point • projection of a point at infinity image plane camera center ground plane

  15. Vanishing points (2D) vanishing point image plane camera center line on ground plane

  16. Vanishing points line on ground plane • Properties • Any two parallel lines have the same vanishing point v • The ray from C through v is parallel to the lines • An image may have more than one vanishing point image plane vanishing point V camera center C line on ground plane

  17. Vanishing lines v1 v2 • Multiple Vanishing Points • Any set of parallel lines on the plane define a vanishing point • The union of all of these vanishing points is the horizon line • also called vanishing line • Note that different planes define different vanishing lines

  18. Vanishing lines • Multiple Vanishing Points • Any set of parallel lines on the plane define a vanishing point • The union of all of these vanishing points is the horizon line • also called vanishing line • Note that different planes define different vanishing lines

  19. Computing vanishing points • Properties • Pis a point at infinity, v is its projection • They depend only on line direction • Parallel lines P0 + tD, P1 + tD intersect at P V P0 D

  20. Computing vanishing lines • Properties • l is intersection of horizontal plane through C with image plane • Compute l from two sets of parallel lines on ground plane • All points at same height as C project to l • points higher than C project above l • Provides way of comparing height of objects in the scene C l ground plane

  21. Fun with vanishing points

  22. Perspective cues

  23. Perspective cues

  24. Perspective cues

  25. Comparing heights Vanishing Point

  26. Measuring height 5.4 5 Camera height 4 3.3 3 2.8 2 1

  27. Computing vanishing points (from lines) • Least squares version • Better to use more than two lines and compute the “closest” point of intersection • Intersect p1q1 with p2q2 v q2 q1 p2 p1

  28. Measuring height without a ruler Y C ground plane • Compute Y from image measurements • Need more than vanishing points to do this

  29. The cross ratio • A Projective Invariant • Something that does not change under projective transformations (including perspective projection) The cross-ratio of 4 collinear points P4 P3 P2 P1 • Can permute the point ordering • 4! = 24 different orders (but only 6 distinct values) • This is the fundamental invariant of projective geometry

  30. Measuring height scene cross ratio t r C image cross ratio b vZ  T (top of object) R (reference point) H R B (bottom of object) ground plane scene points represented as image points as

  31. Measuring height t v H image cross ratio vz r vanishing line (horizon) t0 vx vy H R b0 b

  32. Measuring height v t1 b0 b1 vz r t0 vanishing line (horizon) t0 vx vy m0 b • What if the point on the ground plane b0 is not known? • Here the guy is standing on the box, height of box is known • Use one side of the box to help find b0 as shown above

  33. 3D projective geometry • These concepts generalize naturally to 3D • Homogeneous coordinates • Projective 3D points have four coords: P = (X,Y,Z,W) • Duality • A plane N is also represented by a 4-vector • Points and planes are dual in 3D: N P=0 • Projective transformations • Represented by 4x4 matrices T: P’ = TP, N’ = NT-1

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