Now, imagine you're standing in the middle of a massive sphere, pointing the camera outwards. If you took a photograph, each line of pixels (up/down and left/right) would represent a curved line segment of a great circle on that sphere. And if a pixel is defined by a row and column (or X and Y value) then the position of the light source of that pixel will be the intersection of those two great circles.
Mathematically it's relatively simple to calculate the intersection point of two great circles (we only consider two points and then throw one of those away - an optimisation) and thus the position in space of the pixel's light source. We could map two or more photographs of the same scene (taken from the same place, but at different angles) onto our sphere (from the inside) and with the right manipulations, we'd be able to build a 360 degree * 180 degree panoramic view (i.e. the full sphere).
And what are the right manipulations? They involve matrices, or more specifically, singular value decompositions. If every photograph shares two overlapping points with another photograph (or photographs) then we can test our inner-sphere projection by comparing the angle between the point-pairs in one photograph with the same point-pair in another. They need to be equal angles or else something's likely wrong with our assumption of the lens projection. Given two point-pairs, it's simply a case of using Procrustes analysis (or actually, just a sub-solution of it) to determine a rotation that can be applied to all pixels of one image to align it perfectly with the other image. Once the aligned images wrap around the entire sphere, you might find that the computed scale factor obtained when calibrating the lens was slightly out. So... why bother calibrating in the first place if you already know the lens is rectilinear! Readjust the scale factor and realign the images as necessary. Then fill the remainder of the sphere with your photographs. Simples!