Image completion for view synthesis using Markov random fields and efficient belief propagation
Year: 2013
Type: conference-paper
Abstract: 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.
Authors: Julian Habigt, Klaus Diepold
Institution: Technical University of Munich
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1991490760