Very nice explanation.

I have not seen many replies on algorithms, so here is what I know from a researcher point of view.

In a few words: if you only have a 2D video, then it is a very hard computer vision problem, that has not been solved on the research side.

There is an active benchmark of disparity estimation algorithms (full bibliography at the end of the page). Those algorithms take two pictures and estimate a depth image. From this depth image, it is possible to reconstruct the scene in 3D (but you cannot see what's behind objects). From my experience, this class of algorithms do quite a bad job with real-life images, and have not been applied to video at all.

I've been using optical flows (see a related benchmark) for the development of an Android app (3D Camera) that converts pictures from 2D to 3D, without glasses (check it out!). The optical flow is a more general version of depth estimation (i.e. in any direction, not just left to right motion motion). It has been applied 3D conversion of videos with relative success, I can search for references if you are interested.

From my knowledge & experience, optical flows are the state of the art algorithms to convert 2D pictures/videos to 3D, but they are quite computationally intensive.