Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Where does the depth information come from?

Transcription

1 Binocular Stereo

2 Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image Where does the depth information come from?

3 Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image image 1 image 2 Dense depth map

4 Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image Humans can do it Stereograms: Invented by Sir Charles Wheatstone, 1838

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6 Public Library, Stereoscopic Looking Room, Chicago, by Phillips, 1923

7 Mark Twain at Pool Table", no date, UCR Museum of Photography

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9 Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image Humans can do it Autostereograms:

10 Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image Humans can do it Autostereograms:

11 Basic stereo matching algorithm For each pixel in the first image Find corresponding epipolar line in the right image Examine all pixels on the epipolar line and pick the best match Triangulate the matches to get depth information Simplest case: epipolar lines are corresponding scanlines When does this happen?

12 Simplest Case: Parallel images Image planes of cameras are parallel to each other and to the baseline Camera centers are at same height Focal lengths are the same

13 Simplest Case: Parallel images Image planes of cameras are parallel to each other and to the baseline Camera centers are at same height Focal lengths are the same Then, epipolar lines fall along the horizontal scan lines of the images

14 Essential matrix for parallel images Epipolar constraint: x R = I t = (T, 0, 0) x t The y-coordinates of corresponding points are the same!

15 Depth from disparity X x x z f f O Baseline O B Disparity is inversely proportional to depth!

16 Stereo image rectification

17 Stereo image rectification reproject image planes onto a common plane parallel to the line between optical centers pixel motion is horizontal after this transformation two homographies (3x3 transform), one for each input image reprojection Ø C. Loop and Z. Zhang. Computing Rectifying Homographies for Stereo Vision. IEEE Conf. Computer Vision and Pattern Recognition, 1999.

18 Rectification example

19 Correspondence search Left Right scanline Matching cost disparity Slide a window along the right scanline and compare contents of that window with the reference window in the left image Matching cost: SSD or normalized correlation

20 Correspondence search Left Right scanline SSD

21 Correspondence search Left Right scanline Norm. corr

22 Basic stereo matching algorithm If necessary, rectify the two stereo images to transform epipolar lines into scanlines For each pixel x in the first image Find corresponding epipolar scanline in the right image Examine all pixels on the scanline and pick the best match x Compute disparity x-x and set depth(x) = B*f/(x-x )

23 Failures of correspondence search Textureless surfaces Occlusions, repetition Non-Lambertian surfaces, specularities

24 Effect of window size Smaller window + More detail More noise Larger window + Smoother disparity maps Less detail W = 3 W = 20

25 Results with window search Data Window-based matching Ground truth

26 How can we improve window-based matching? The similarity constraint is local (each reference window is matched independently) Need to enforce non-local correspondence constraints

27 Non-local constraints Uniqueness For any point in one image, there should be at most one matching point in the other image

28 Non-local constraints Uniqueness For any point in one image, there should be at most one matching point in the other image Ordering Corresponding points should be in the same order in both views

29 Non-local constraints Uniqueness For any point in one image, there should be at most one matching point in the other image Ordering Corresponding points should be in the same order in both views Ordering constraint doesn t hold

30 Non-local constraints Uniqueness For any point in one image, there should be at most one matching point in the other image Ordering Corresponding points should be in the same order in both views Smoothness We expect disparity values to change slowly (for the most part)

31 Scanline stereo Try to coherently match pixels on the entire scanline Different scanlines are still optimized independently Left image Right image

32 Stereo as energy minimization Find disparity map d that minimizes an energy function Simple pixel / window matching = SSD distance between windows I(x, y) and J(x + d(x,y), y)

33 Stereo as energy minimization I(x, y) J(x, y) y = 141 d x C(x, y, d); the disparity space image (DSI)

34 Stereo as energy minimization y = 141 d x Simple pixel / window matching: choose the minimum of each column in the DSI independently:

35 Stereo as energy minimization y = 141 d x

36 Stereo as energy minimization y = 141 d x

37 Stereo as energy minimization y = 141 d x

38 Stereo as energy minimization d x 38

39 Stereo as energy minimization Better objective function { match cost { smoothness cost Want each pixel to find a good match in the other image Adjacent pixels should (usually) move about the same amount

40 Stereo as energy minimization match cost:

41 Stereo as energy minimization match cost: smoothness cost: : set of neighboring pixels Left and right

42 Smoothness cost How do we choose V? L 1 distance Potts model

43 Stereo as energy minimization y = 141 d x

44 Stereo as energy minimization y = 141 d x

45 Stereo as energy minimization y = 141 d x

46 Dynamic programming Can minimize this independently per scanline using dynamic programming (DP) : minimum cost of solution such that d(x,y) = d

47 Dynamic programming y = 141 d x

48 Dynamic programming y = 141 d x Cost of optimal path from the left

49 Dynamic programming y = 141 d x How to compute for this pixel?

50 Dynamic programming y = 141 d x How to compute for this pixel? Only 3 ways to get here...

51 Dynamic programming y = 141 d x This cost is...

52 Dynamic programming y = 141 d x This cost is D Ed Es

53 Dynamic programming y = 141 d x Fill D all the way to the right

54 Dynamic programming y = 141 d x Pick the best one from the right column

55 Dynamic programming y = 141 d x Back track...

56 Dynamic programming y = 141 d x Back track...

57 Stereo as energy minimization y = 141 d x

58 Dynamic Programming

59 Dynamic programming is optimal 59

60 Coherent stereo on 2D grid Scanline stereo generates streaking artifacts Can t use dynamic programming to find spatially coherent disparities/ correspondences on a 2D grid

61 Stereo as energy minimization match cost: smoothness cost: : set of neighboring pixels Left and right

62 Stereo as energy minimization match cost: smoothness cost: : set of neighboring pixels 4-connected neighborhood

63 Pixelwise

64 Pixelwise

65 Pixelwise -> Scanline wise y = 141

66 Pixelwise -> Scanline wise y = 141

67 Pixelwise -> Scanline wise y = 141

68 Pixelwise

69 Pixelwise

70 Stereo as energy minimization match cost: smoothness cost: : set of neighboring pixels 4-connected neighborhood 8-connected neighborhood

71 Smoothness cost How do we choose V? L 1 distance

72 Stereo matching as energy minimization I 1 I 2 D Graph-cuts can be used to minimize such energy Y. Boykov, O. Veksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001

73 Same formulation with more images Change label from disparity to depth Change Ed(d) by using more images...

74 Same formulation with more images Change label from disparity to depth Change Ed(d) by using more images

75 Same formulation with more images Change label from disparity to depth Change Ed(d) by using more images...

76 Same formulation with more images Change label from disparity to depth Change Ed(d) by using more images Cost of assigning this depth comes from

77 With more images, engineering and tricks 77

78 Active stereo with structured light Project structured light patterns onto the object Simplifies the correspondence problem Allows us to use only one camera camera projector L. Zhang, B. Curless, and S. M. Seitz. Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming. 3DPVT 2002

79 Active stereo with structured light L. Zhang, B. Curless, and S. M. Seitz. Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming. 3DPVT 2002

80 Active stereo with structured light

81 Kinect: Structured infrared light

82 Kinect fushion 82

83 Laser scanning Digital Michelangelo Project Levoy et al. Optical triangulation Project a single stripe of laser light Scan it across the surface of the object This is a very precise version of structured light scanning Source: S. Seitz

84 Laser scanned models The Digital Michelangelo Project, Levoy et al. Source: S. Seitz

85 Laser scanned models The Digital Michelangelo Project, Levoy et al. Source: S. Seitz

86 Laser scanned models The Digital Michelangelo Project, Levoy et al. Source: S. Seitz

87 Laser scanned models The Digital Michelangelo Project, Levoy et al. Source: S. Seitz

88 Laser scanned models 1.0 mm resolution (56 million triangles) The Digital Michelangelo Project, Levoy et al. Source: S. Seitz

89 Aligning range images A single range scan is not sufficient to describe a complex surface Need techniques to register multiple range images B. Curless and M. Levoy, A Volumetric Method for Building Complex Models from Range Images, SIGGRAPH 1996

90 Aligning range images A single range scan is not sufficient to describe a complex surface Need techniques to register multiple range images which brings us to multi-view stereo