Northwestern University Spring 2002 Jack Tumblin
Grading: 5 Self-Contained Programming Projects,
1 Written Midterm Exam.
(I changed my mind: no final exam):
| Filename Prefix | Topic | Due Date | Weight |
| p1<your last name> | Image Grid Viewer | Tues April 16 | 5% |
| p2<your last name> | 2D Projective Image Warper | Tues May 2 | 15% |
| (handouts, paper) | Homework | alternate weeks | 20% |
| p3<your last name> | 3D Projective Image Warper | Tues May 14 | 20% |
| p4<your last name> | Monocular Camera Calibrator | Tues May 28 | 20% |
| p5<your last name> | 2-Image Epipolar Geometry Finder | Exam Day | 20% |
--Please don't skip class to work on your project:
instead, come to class and ask/learn how to do the hard
parts. It's far more efficient.
DRAFT:
Now we will add some depth data to our mesh image.
1) Use this image of a shaded cube (or tetrahedron, or make your own) as the
texture map for a mesh image.
2) Set some depth values. Choose few mesh points at the corners of the
cube in the image, and manually and give them some plausible non-zero depth
values. Use linear interpolation between these depths to find depth for all the
in-between image mesh points. Set large background depth values; for example,
you might set them all to a value twice as large as the initial distance from
the camera to the cube center.
3) Set the camera to view the mesh image as we did in Project 1: 'head-on', so
the mesh image = screen image.
4) Without moving the camera, move the mesh image vertices in 2D (no change in
depth), so as to give the appearance of viewing the image from a different
camera position (position B).
5) As with Project 1, repeat step 4, but now move the camera around to a
different position (position B will be interesting!) so you can see what your
warping does from a different angle.
DRAFT:
Read in a mesh image of a cube again, (or anything with at least three large non-coplanar polygons with visible vertices). Manually mark mesh image the corners of each visible cube side. From this data, find the camera matrix that was used to make the image! Extra credit: estimate error bounds (error ellipsoids, etc.).
DRAFT:
Now read in TWO mesh images A,B that are photos of the same 3D scene taken from mildly different camera positions (most of the things visible in image A are visible in image B). Mark a few corresponding points in the images. Find the two camera positions and fundamental matrix, and display them as objects in 3D. Extra Credit: Move the image meshes to camera positions in 3D, set depth values for the vertex correspondences. Show epipolar planes. Search along epipolar lines in the mesh images to find more correspondences.