  X-ray tomography is a powerful volumetric imaging technique, but detailed
three dimensional (3D) imaging requires the acquisition of a large number of
individual X-ray images, which is time consuming. For applications where
spatial information needs to be collected quickly, for example, when studying
dynamic processes, standard X-ray tomography is therefore not applicable.
Inspired by stereo vision, in this paper, we develop X-ray imaging methods that
work with two X-ray projection images. In this setting, without the use of
additional strong prior information, we no longer have enough information to
fully recover the 3D tomographic images. However, up to a point, we are
nevertheless able to extract spatial locations of point and line features. From
stereo vision, it is well known that, for a known imaging geometry, once the
same point is identified in two images taken from different directions, then
the point's location in 3D space is exactly specified. The challenge is the
matching of points between images. As X-ray transmission images are
fundamentally different from the surface reflection images used in standard
computer vision, we here develop a different feature identification and
matching approach. In fact, once point like features are identified, if there
are limited points in the image, then they can often be matched exactly. In
fact, by utilising a third observation from an appropriate direction, matching
becomes unique. Once matched, point locations in 3D space are easily computed
using geometric considerations. Linear features, with clear end points, can be
located using a similar approach.
