## Description

Camera Calibration:

1) Load the 100 pairs of corresponding 2-D and 3-D points in the files 2Dpoints.txt and

3Dpoints.txt (the i

th row of both files corresponds to the i

th point). Use these point

correspondences to solve (using Eigen-analysis) for the camera matrix P (whose

rasterized vector p has a unit L2 norm). [5 pts]

2) Given the computed matrix P (from Problem 1), project the 3-D homogeneous points

(Xi,Yi,Zi,1) to 2-D. Compute the sum-of-squared error (sum-of-squared distances)

between the resulting 3-D-to-2-D projected points and the given 2-D points (ensure

all 2-D points are inhomogeneous). [3 pts]

Homography:

3) The file homography.txt contains 15 corresponding 2-D points from two different

images, where the first and second columns correspond to the x and y coordinates of

the points in the first image and the third and fourth columns correspond to the x and

y coordinates of the points in the second image. Load the 2-D point sets and use the

Normalized Direct Linear Transformation algorithm to compute the final

homography H that maps the original points from image 1 to image 2 (i.e., make sure

P2 = HP1). [5 pts]

4) Plot the points from image 2 and the projected points from image 1 on the same plot.

Make sure the projected points are converted into inhomogeneous form. [1 pt]

5) Compute the sum-of-squared error (squared Euclidean distance) between the actual

points from image 2 and the projected points from image 1. [2 pts]

6) As usual, turn in and upload your material.