Sam Saltwick
October 23 2018
Fall 2018
- 2D images project 3D points into 2D
- 3D points on the same viewing line have the same 2D image
- 2D imaging results in depth information loss
- Assumes two cameras with known positions
- Can recover depth from this information
- Depth recovered with two images and triangulation
- Find correspondences between images and see where their projective lines meet
- Solution is not always unique
- Looking at 3 points -> 9 intersections in space -> 3 possible solutions
- Find Correspondences and epipolar lines
- Epipolar lines -> lines formed by the intersection between the plane created by 2 correspondences and their intersection and the camera plane
- Reduces correspondence problem to 1D search in conjugate epipolar lines
- Image planes of cameras are parallel
- Focal points are at the same height
- Focal lengths are the same
- => Epipolar lines are horizontal scan lines
- T is the stereo baseline
- d measures the difference in retinal position between correspondences
- Given Z we can compute X and Y
- Objects? Edges? Pixels? Collections of pixels?
- Slide window along scanline and compare its contents with the reference window in the other image
- Matching Cost: SSD or normalized correlation
- Minimize SSD or Maximized Correlation
- Correspondence at the minimum point of the matching cost
- Effects of Window Size
- Window size too small -> A lot of noise
- Window size too big -> Too much smoothing => loss of detail