CV Chapter 7 3D Reconstruction
Questions about the lecture 'Computer Vision' of the RWTH Aachen Chapter 7 3D Reconstruction
Questions about the lecture 'Computer Vision' of the RWTH Aachen Chapter 7 3D Reconstruction
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| Karten | 108 |
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| Sprache | English |
| Kategorie | Informatik |
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| Erstellt / Aktualisiert | 04.02.2017 / 23.02.2017 |
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How does it works?
[proj factorization.projective.SfM.3D resconstruction, 3]
1. If depth z is known then factorize D to estimate M and S
2. If M and S are known then solve for depth z
3. Use iterative method alternating between above two steps
How does it works?
[seq struture.projective.SfM.3D resconstruction, 2]
1. Initialize motion from two images with F and structure
2. For each additional view perform calibration, triangulation and bundle adjustment
How does it works?
[calibration.seq struture.projective.SfM.3D resconstruction]
Determine projective matrix via additional points
How does it works?
[triangulation.seq struture.projective.SfM.3D resconstruction]
Refine structure re and computing points
How does it works?
[bundle adjustement.seq struture.projective.SfM.3D resconstruction, 4]
1. Refine structure and motion
2. Non-linear method
3. Minimize mean-square reprojection error E(P,X) = Sumi=1mSumi=jnD(xij,PiXj)²
4. Seeks maximum likelihood assuming Gaussian noise
What is the characteristic?
[bundle adjustement.seq struture.projective.SfM.3D resconstruction]
Generally used as final step of any multi-view reconstruction algorithm with good initialization
How to solve projective ambiguity?
[projective.SfM.3D resconstruction, 3]
1. Do not solve, can already be useful answering lines intersecting with planes
2. Euclidean upgrade with new knowledge of calibration or markers
3. Self-calibration
How does it work?
[self-calibration.projective.SfM.3D resconstruction, 2]
1. Determine intrinsic parameters from uncalibrated images
2. Constraint that parameters of one camera remain fixed
What are the practical considerations?
[projective.SfM.3D resconstruction, 3]
1. Baseline
2. Apply RANSAC // Incorrect matches or moving objects
3. Estimation dependent on point location // Far points stable
What are the characteristics?
[baseline.prac considerations.projective.SfM.3D resconstruction, 3]
1. Small yields large depth error
2. Large yields difficult search problem
3. Track feautres between frames until sufficient
What are the guidelines?
[projective.SfM.3D resconstruction, 3]
1. Use calibrated cameras wherever possible
2. Perform SfM with two cameras
3. Any constraint on setup can be useful
What are possible ones?
[constraints.guidelines.projective.SfM.3D resconstruction, 3]
1. Square pixel, zero skew, fixed focal length
2. Fixed baseline in stereo SfM setup
3. Constrained camera motion on a ground plane
What holds?
[constraints.guidelines.projective.SfM.3D resconstruction]
Might need adapting algorithm
What are limitations?
[SfM.3D reconstruction, 3]
1. Difficult for large motion or field-of-view and depth variation
2. Limited camera calibration
3. Good feature trackers required
Name commercial SW packages?
[SfM.3D reconstruction, 7]
1. boujou
2. PFTrack
3. MatchMover putting virtual objects in videos
4. SynthEyes
5. Icarus
6. Voodoo camera tracker
7. Large-scale SfM by Flickr
What are cues for 3D recovery? [5]
1. Shading
2. Texture
3. Focus
4. Perspective
5. Motion
Which kind of geometry is necessary for stereo vision?
Multi-view geometry
What is the generic problem formulation for stereo vision?
“Given several images of an object or scene, compute 3D shape”
What is the narrower problem formulation for stereo vision?
“Given calibrated binocular stereo pair, fuse it to depth image”
What is the definition of triangulation in epipolar geometry?
Reconstruction as intersection of two rays
What is required for triangulation in epipolar geometry? [2]
1. Point correspondence
2. Camera pose or calibration
What are the two different set of parameters for camera calibration in triangulation in epipolar geometry? [2]
1. Extrinsic and 2. intrinsic
What does the extrinsic parameters describe for camera calibration in triangulation in epipolar geometry?
Camera ↔ reference frame
What does the intrinsic parameters describe for camera calibration in triangulation in epipolar geometry?
Image ↔ pixel coordinates // relative
What are the extrinsic parameters for camera calibration in triangulation in epipolar geometry? [2]
1. Rotation matrix
2. Translation vector
What are the intrinsic parameters for camera calibration in triangulation in epipolar geometry? [6]
1. Focal length
2. Pixel sizes [mm]
3. Image center point
4. Radial distortion
5. Pixel magnification factors
6. Skew (non-rectangular pixels)
Name the list of attributes necessary if we have parallel optical axes and known camera parameters in a simple stereo system? [6]
1. World point p and image point left pl and right pr
2. xl and xr
3. Focal length f
4. Optical center left Ol and right Or
5. Baseline T
6. Depth Z of p
How does triangulation works if we have parallel optical axes and known camera parameters in a simple stereo system? [4]
1. (pl, p, pr) and (Ol, p, Or)
2. (T-(xr-xl))/(Z-f) = T/Z
3. Z = f*T/(xr-xl) with (xr-xl) disparity
4. Update (x’,y’) with disparity map (x+D(x,y), y)
How do we proceed if optical axes are not parallel in a simple stereo system? [3]
1. Image scanlines are epipolar lines
2. Re-project image on plane parallel to optical centers // Stereo image rectification
3. Two homographies (3x3 transforms) are necessary
What is the definition of a baseline T in epipolar geometry?
Line joining camera centers
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