  Panorama creation is one of the most widely deployed techniques in computer
vision. In addition to industry applications such as Google Street View, it is
also used by millions of consumers in smartphones and other cameras.
Traditionally, the problem is decomposed into three phases: registration, which
picks a single transformation of each source image to align it to the other
inputs, seam finding, which selects a source image for each pixel in the final
result, and blending, which fixes minor visual artifacts. Here, we observe that
the use of a single registration often leads to errors, especially in scenes
with significant depth variation or object motion. We propose instead the use
of multiple registrations, permitting regions of the image at different depths
to be captured with greater accuracy. MRF inference techniques naturally extend
to seam finding over multiple registrations, and we show here that their energy
functions can be readily modified with new terms that discourage duplication
and tearing, common problems that are exacerbated by the use of multiple
registrations. Our techniques are closely related to layer-based stereo, and
move image stitching closer to explicit scene modeling. Experimental evidence
demonstrates that our techniques often generate significantly better panoramas
when there is substantial motion or parallax.
