  View synthesis is a process for generating novel views from a scene which has
been recorded with a 3-D camera setup. It has important applications in 3-D
post-production and 2-D to 3-D conversion. However, a central problem in the
generation of novel views lies in the handling of disocclusions. Background
content, which was occluded in the original view, may become unveiled in the
synthesized view. This leads to missing information in the generated view which
has to be filled in a visually plausible manner. We present an inpainting
algorithm for disocclusion filling in synthesized views based on Markov random
fields and efficient belief propagation. We compare the result to two
state-of-the-art algorithms and demonstrate a significant improvement in image
quality.
