Description
1 Panoramic Stitching
In this problem we will develop an algorithm for stitching a panorama from overlapping photos (Figure
1), which amounts to estimating a transformation that aligns one image to another. To do this, we will
compute SURF/SIFT/ORB features in both images and match them to obtain correspondences. We will
then estimate a homography from these correspondences, and we’ll use it to stitch the two images together
in a common coordinate system. In order to get an accurate transformation, we will need many accurate
feature matches. Unfortunately, feature matching is a noisy process: even if two image patches (and their
SURF/SIFT/ORB descriptors) look alike, they may not be an actual match. To make our algorithm robust
to matching errors, we will use RANSAC, a method for estimating a parametric model from noisy observations. We will use the obtained homography transformation to do panoramic stitching. We have provided
you with two input images and also starter code.
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(a) Image pair (b) Stitched panorama
Figure 1: Panorama produced using our implementation
Tasks
1. Implement get features(img) to compute SURF/SIFT/ORB features for both of the given image.
Implement match keypoints(desc1, desc2) to compute key-point correspondences between the two
source images using the ratio test. Run the plotting code to visualize the detected features and resulting
correspondences. (Hint: You can use existing libraries.)
2. Write a function find homography(pts1, pts2) that takes in two N×2 matrices with the x and
y coordinates of matching 2D points in the two images and computes the 3×3 homography H that
maps pts1 to pts2. You can implement this function using direct linear transform (DLT). Report the
homography matrix. (Hint: You should implement this function on your own)
3. Your homography-fitting function from (2) will only work well if there are no mismatched features. To
make it more robust, implement a function transform ransac(pts1, pts2) that fits a homography
using RANSAC. Run the plotting code to visualize the point correspondences after applying RANSAC.
(Hint: You should implement this function on your own)
4. Write a function panoramic stitching(img1, img2) that produces a panorama from a pair of overlapping images using your functions from the previous parts. Run the algorithm on the two images
provided. Report the panorama stitched image. (Hint: You should implement this function on your
own. Use inverse mapping while stitching.)
5. Extend the algorithm to handle n=3 images, use these given images. I have also provided reference
panoramic stitched image to compare your result with. Report the all visualizations as mentioned
for n=2 case, along with two homography matrices with respect to middle image.(Hint: You should
implement this function on your own. Take the middle image as reference and find the homography
transformations with respect to middle image, that doesn’t need further transformations and also produce
the most aesthetically pleasing panorama.)
Note: You should report your results for each task. Except for task-2 provide visualizations,
for task-2 report homography matrix. For task-5 also you should report all the visualizations
along with two homography matrices with respect to middle image.
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