Machine Vision problem - Photo matching. Is a solution possible / known, using OpenCV?

Having searched around on SO, and also checked on OpenCV list but not having found an answer, posting my query here.

Problem: Match 2 photos of the same scene, shot from 2 slightly different camera angles, and with slightly different lens distortions, with slightly different zoom-levels, and shot under slightly different lighting conditions.

Constraints:

Slightly different in the above statements can be taken to mean max. of 10% in most cases.
The scene in question is to be considered an indoor scene, or an outdoor seen with limited details.
Matching accuracy of 75% would be acceptable.
The photos aren't high-resolution (shot with consumer grade mostly budget / cell-phone cameras)

What gives me the hope that this problem might be solvable is the existence of software that stitches photos to create panoramas. They seem to figure out the overlapping sections auto-magically. They do so even when the horizon orientations don't match exactly, slight differences exist in exposure level or background illumination, and minor zoom level differences exist. I think, what I need is a very similar workflow and set of algorithms.

Note that while my question might seem similar to one here, actually it is not.

You need to compute the homography between the images which needs point correspondences such as SURF interest points.

Once you have the homography, you can do a projective transformation of the images so that they match up. Following that, you can try some sort of blending at the seams to make it look seamless.

This paper describes it pretty well. You can replace the Multi-scale Oriented Patches used by Szeliski with SURF interest points. Here are some more resources to get you started: