Description
1. Stereo Matching Costs
(a) [0.5 point] Imagine that two images for stereo matching are captured by the same camera
but under different exposure times. Compare the two main cost functions for matching,
i.e. SSD and NC, for estimating the correspondences in these two images. Which one
performs better and why?
2. Stereo Matching Implementation – for this question, use the rectified image pair
(000020_left.jpg and 000020_right.jpg), the given bounding box in file 000020.txt, and the given
parameters in file 000020_allcalib.txt.
(a) [2 points] Write a program to compute the depth for each pixel in the given bounding box
of car. Use the algorithm given in class, where given a left patch, compare it with all the
patches on the right image’s scanline. To reduce computation complexity, you can try to
use a small patch size, or sample patches (e.g. every other pixel) from scanline instead of
comparing with all possible patches. Report what is the patch size, sampling method, and
matching cost function you used. Use the parameters given and show how depth is
computed for each pixel. Also visualize the depth information. Are there any outliers
coming from incorrect point correspondences?
(b) [2 points] After you compute depth using scanline (above), go to KITTI Stereo 2015
(http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=stereo), and
pick a machine learning model from the scoreboard. You can pick a model that comes
with code and a pre-trained model so you don’t need to implement yourself. Compute
the depth for the whole image using their pretrained model. Compare your results from
the previous questions with this results from this model. What is the difference, both in
terms of quality and speed?
(c) [1 point] Write a short summary of workflow for the model you pick, e.g. what layers do
they use, what modules do they use, etc.
(d) [1.5 points] Using the depth information from models, try to determine within the
bounding box, which pixel belong to the car based on its distance to the box center pixel’s
3D location (use a threshold). After that, try to determine a 3D bounding box for the car
(min & max along X, Y, Z). Visualize the segmentation of pixels within the 2D box, and on
another image visualize the 3D box. State the classification threshold for distance.
3. Fundamental Matrix – for this question, take 3 images (I1, I2, I3) of an object or a stationary
scene as follows: Images I1, I2 from almost the same viewpoint, but with a (roll) rotation. That is,
take an image (I1), don’t move, but rotate the camera in place around 30-45 degrees, and take
another image (I2). Take the third image (I3) from a different viewpoint, e.g. move the camera
~20 cm to the right and rotate (out of plane) to point the camera towards the object again.
I1 and I2:
I1 and I3 (top view):
(a) [1 points] Use SIFT matching (or any other point matching technique) to find a number of
point correspondences in the (I1 , I2) image pair and in the (I1 , I3) image pair. Visualize the
results. If there are any outliers, either manually remove them or increase the matching
threshold so no outliers remain. Pick 8 point correspondences from the remaining set for
each image pair, i.e. (I1 , I2) and (I1 , I3). Visualize those 8 point matches. It helps in the
later steps if the 8 point matches are somewhat distributed over the images rather than
being clustered in a small region.
(b) [1 point] Using what we have learned in class (standard 8-point algorithm) calculate the
fundamental matrix F12 for image pair (I1 , I2) and Fundamental matrix F13 for image pair
(I1 , I3). (for this question, implement your own standard 8-point algorithm)
(c) [1 point] Using F12, calculate the epipolar lines in the right image for each of the 8 points
in the left image and plot them on the right image. (for this question you can use any
OpenCV functions you want)
(d) [1 point] Using F12 , rectify I2 with I1 and visualize the resulting image side by side with I1.
Do the same for I3 using F13 . (for this question you can use any OpenCV functions you
want)
(e) [0.5 point] Using OpenCV, compute F’12 and F’13 and compare with your results. I.e. are
they same? Are they similar? Briefly discuss.
(f) [0.5 point] Using OpenCV, rectify the images using F’12 and F’13 and compare with your
rectifications (part d). Discuss any differences.
