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Q:

What calibration object is used in Zhang’s method?

A:

One planar grid

Q:

For a calibrated camera, what is the minimum number of points required by the PnP algorithm to obtain one unique solution?

A:

4

Q:

Which statement is correct? ⊆ means "is a particular case of"?

A:

VO ⊆ SLAM ⊆ SfM

Q:

Which of the following feature detectors is scale invariant?

A:

SIFT

Q:

In which of the following cases can you recover the metric scale?

A:

Stereo

Q:

For the same set of inputs, RANSAC always provides the same result.

A:

False

Q:

(2 P.) How many solutions will the PnP algorithm give if you have one, two, three, and four 3D-2D correspondences?

A:

1: infinite

2: infinite, but bounded

3: up to 4

4: one solution

Q:

Mark the correct output c of the convolution between the 1D image a and the 1D filter b using zero padding. The output should have the same size as the input image.

b = [2, 1, 2]

a = [3, 1, 2]

c = a * b

A:

c = [5,11,4]

Q:

What is the minimum number of correspondences required for general (i.e. unconstrained) structure from motion with a calibrated camera?

A:

5

Q:

What would be the closest depth measured by a stereo camera with baseline b and focal length f and resolution of W × H (W = width and H = height)? Assume a simplified and rectified stereo setup in 2D, i.e., both cameras have identical intrinsic parameters and both image planes are coplanar and aligned with the baseline.

A:

bf/W

Q:

(3 P.) Define the PnP problem for a calibrated camera by stating the known and unknown entities.

A:

PNP problem: problem of determining the 6DoF pose of a camera (position and orientation) with respect to the world frame from a set of 3D-2D point correspondences.

unknown: R, T

Q:

What is the minimum number of 2D point correspondences necessary to determine the 2D transformation between two images of the same planar object?

A:

5

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