An overview on image warping techniques for 2D images is beyond
the scope of this paper. Good surveys on image warping are
available [Wolberg 1990; Gomes et al. 1999; Szeliski 2010]. This
section focuses on stereoscopic image authoring and processing.
Disparity control is important for creating high-quality stereoscopic
images and videos. The disparity needs to be properly distributed
so that the scene content exists in the stereoscopic comfort
zone [Howard and Rogers 2002; Hoffman et al. 2008]. Algorithms
have been developed to determine the camera parameters [Jones
et al. 2001; Mueller et al. 2008; Koppal et al. 2011; Zilly et al.
2010] before capture. Recently, Heinzle et al. [2011] built a computational
stereo camera system that closes the control loop from
capture and analysis to automatic setting of these parameters. Oskam
et al. [2011] developed a system for stereoscopic camera control
in interactive 3D applications.
Disparity editing tools have also been developed for postproduction.
Wang and Sawchuk [2008] presented a framework for
disparity editing that either directly works on the dense disparity
map or assumes known camera parameters and applies image-based
rendering methods to novel view synthesis. Lang et al. developed a
set of disparity mapping tools to control the disparity distribution in
a nonlinear and locally adaptive fashion [Lang et al. 2010; Smolic
et al. 2011]. Koppal et al. [2011] developed a viewer-centric editor
for stereoscopic movies that provides tools for both shot planning
and disparity editing. Didyk et al. [2011] introduced a perceptual
model of disparity for computer graphics and applied to a number