Stereo Vision Computer Vision (Kris Kitani) Carnegie Mellon University

Transcription

1 Stereo Vision Computer Vision (Kris Kitani) Carnegie Mellon University

2 What s different between these two images?

3

4

5 Objects that are close move more or less?

6 The amount of horizontal movement is inversely proportional to

7 The amount of horizontal movement is inversely proportional to the distance from the camera.

8 X image plane O O

9 X x f x image plane f O O

10 X Z f x x f O O

11 X Z f x x O O (baseline) b f

12 X X Z = x f Z f x x O O (baseline) b f

13 X X Z = x f Z b Z X = x f f x x O O (baseline) b f

14 X X Z = x f Z b Z X = x f f x x O O (baseline) b f Disparity d = x x = bf Z

15 X X Z = x f Z b Z X = x f f x x O O (baseline) b f Disparity d = x x inversely proportional to depth = bf Z

16 Real-time stereo sensing Nomad robot searches for meteorites in Antartica

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18 Subaru Eyesight system Pre-collision braking

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20 How so you compute depth from a stereo pair?

21 1. Rectify images (make epipolar lines horizontal) 2. For each pixel a. Find epipolar line b. Scan line for best match c. Compute depth from disparity Z = bf d

22 How can you make the epipolar lines horizontal?

23 It s hard to make the image planes exactly parallel

24 How can you make the epipolar lines horizontal?

25 How can you make the epipolar lines horizontal? When this relationship holds: R = I t = (T,, ) x t x

26 How can you make the epipolar lines horizontal? When this relationship holds: R = I t = (T,, ) x x Let s try this out E = t R = & $ $ $ % T T #!!!" t This always has to hold x T E x" =,

27 t x x How can you make the epipolar lines horizontal? R = I t = (T,, )!!! " # $ $ $ % & = = T T R t E ( ) ( ) Tv T v u v u T T v u = " " " # $ % % % & '! = " " " # $ % % % & '!! ) ) ) * +,,, E x x T = ", Write out the constraint Let s try this out This always has to hold When this relationship holds:

28 t x x How can you make the epipolar lines horizontal? R = I t = (T,, )!!! " # $ $ $ % & = = T T R t E ( ) ( ) v T Tv Tv T v u v u T T v u! = = " " " # $ % % % & '! = " " " # $ % % % & '!! ) ) ) * +,,, E x x T = ", y coordinate is always the same! Write out the constraint Let s try this out This always has to hold The image of a 3D point will always be on the same horizontal line When this relationship holds:

29 What is stereo rectification?

30 What is stereo rectification?

31 What is stereo rectification? Reproject image planes onto a common plane parallel to the line between camera centers

32 What is stereo rectification? Reproject image planes onto a common plane parallel to the line between camera centers Need two homographies (3x3 transform), one for each input image reprojection C. Loop and Z. Zhang. Computing Rectifying Homographies for Stereo Vision.Computer Vision and Pattern Recognition, 1999.

33 Stereo Rectification 1. Rotate the right camera by R (aligns camera coordinate system orientation only) 2. Rotate (rectify) the left camera so that the epipole is at infinity 3. Rotate (rectify) the right camera so that the epipole is at infinity 4. Adjust the scale

34 Stereo Rectification: 1. Compute E to get R 2. Rotate right image by R 3. Rotate both images by Rrect 4. Scale both images by H

35 Stereo Rectification: rotate by R 1. Compute E to get R 2. Rotate right image by R 3. Rotate both images by Rrect 4. Scale both images by H

36 Stereo Rectification: 1. Compute E to get R 2. Rotate right image by R 3. Rotate both images by Rrect 4. Scale both images by H

37 Stereo Rectification: rotate by Rrect rotate by Rrect 1. Compute E to get R 2. Rotate right image by R 3. Rotate both images by Rrect 4. Scale both images by H

38 Stereo Rectification: 1. Compute E to get R 2. Rotate right image by R 3. Rotate both images by Rrect 4. Scale both images by H

39 Stereo Rectification: scale by H scale by H 1. Compute E to get R 2. Rotate right image by R 3. Rotate both images by Rrect 4. Scale both images by H

40 Stereo Rectification: 1. Compute E to get R 2. Rotate right image by R 3. Rotate both images by Rrect 4. Scale both images by H

41 What can we do after rectification?

42 Setting the epipole to infinity (Building R rect from e) Let R rect = 2 4 r > 1 r > 2 r > Given: epipole e (using SVD on E) (translation from E) r 1 = e 1 = r 2 = T T 1 q Tx 2 + Ty 2 epipole coincides with translation vector T y T x cross product of e and the direction vector of the optical axis r 3 = r 1 r 2 orthogonal vector

43 r 1 = e 1 = T If and r 2 r 3 orthogonal T then R rect e 1 = 2 4 r > 1 e 1 r > 2 e 1 r > 3 e = 2 4??? 3 5

44 r 1 = e 1 = T If and r 2 r 3 orthogonal T then R rect e 1 = 2 4 r > 1 e 1 r > 2 e 1 r > 3 e = Where is this point located on the image plane?

45 r 1 = e 1 = T If and r 2 r 3 orthogonal T then R rect e 1 = 2 4 r > 1 e 1 r > 2 e 1 r > 3 e = Where is this point located on the image plane? At x-infinity

46 Stereo Rectification Algorithm 1. Estimate E using the 8 point algorithm (SVD) 2. Estimate the epipole e (SVD of E) 3. Build Rrect from e 4. Decompose E into R and T 5. Set R1=Rrect and R2 = RRrect 6. Rotate each left camera point (warp image) [x y z ] = R1 [x y z] 7. Rectified points as p = f/z [x y z ] 8. Repeat 6 and 7 for right camera points using R2

47 Stereo Rectification Algorithm 1. Estimate E using the 8 point algorithm 2. Estimate the epipole e (solve Ee=) 3. Build Rrect from e 4. Decompose E into R and T 5. Set R1=Rrect and R2 = RRrect 6. Rotate each left camera point x ~ Hx where H = KR1 *You may need to alter the focal length (inside K) to keep points within the original image size 7. Repeat 6 and 7 for right camera points using R2

48 Unrectified

49 Unrectified Rectified

50 Unrectified Rectified