IMAGE COMPLETION FOR VIEW SYNTHESIS USING MARKOV RANDOM FIELDS AND EFFICIENT BELIEF PROPAGATION
Julian Habigt, Klaus J. Diepold, Julian Habigt, Klaus J. Diepold · 2016
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 syn-thesized 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.