Photogrammetry is about geometric modeling of real world from images. By the same token, computer graphics is about geometric modeling for scene rendering. Both photogrammetry & computer graphics share the core fundamentals of geometrical modeling and they can leverage each other. Indeed, computer vision fills this need for computer graphics in terms of supplying the geometric world. On the other hand, the mind set and the tools of photogrammetry could serve a critical needs for this world in terms of accuracy and visual fidelity. In fact, the crunching machine of 3D photogrammetric mapping can be deployed to a shear amount of application in computer graphics. I'm asking this kind of questions to stimulate more ideas on how we can push photogrammetry into a new horizon of applications.
This is a good question with many possible answers.
In terms of the integration of photogrammetry and computer vision, see, for example, where computer graphics is mentioned:
L. Barazzetti, F. Remondino, M. Scaioni, Combined use of photogrammetric and computer vision techniques for fully automated and accurate 3D modelling of terrestrial objects (from L. Barazzetti's RG webpage).
A thoroughgoing study of computer graphics and its relation to photogrammetry is given in
S.R. Marschner, Inverse Rendering for Computer Graphics, Ph.D. thesis, Cornell University, 1998:
http://graphics.stanford.edu/~srm/thesis/thesis.pdf
Creating realistic images is a major focus in the study of computer graphics (page 1). Radiometry and inverse rendering are concerned with the propagation of light. The fundamental radiometric quantity is radiance, which describes the flow of light through space (a central interest in photogrammetry). See Section 2.2, starting on page 5, on radiometry.
A central problem in photogrammetry is how to improve images and faithful rendering of perceived scenes. The solution to this problem leads to the inverse rendering problem, which requires a description of the sources of light that illuminated an object when a photograph was taken. See Section 3.3, starting on page 19, on the least squares solution to the inverse rendering problem. See, especially, Fig. 3.1, which exhibits the data flow in the inverse lighting algorithm. See, also, Figures 3.3, 3.4 and 3.5, staring on page 27, for flow graphs for a re-lighting system. The penultimate chapter in this thesis is Chapter 4 (Photographic Texture Measurement), starting on page 41.
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