Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Artificial Intelligence for Efficient Image-based View Synthesis


Leimkühler,  Thomas
Computer Graphics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Leimkühler, T. (2019). Artificial Intelligence for Efficient Image-based View Synthesis. PhD Thesis, Universität des Saarlandes, Saarbrücken. doi:10.22028/D291-28379.

Cite as: http://hdl.handle.net/21.11116/0000-0004-A589-7
Synthesizing novel views from image data is a widely investigated topic in both computer graphics and computer vision, and has many applications like stereo or multi-view rendering for virtual reality, light field reconstruction, and image post-processing. While image-based approaches have the advantage of reduced computational load compared to classical model-based rendering, efficiency is still a major concern. This thesis demonstrates how concepts and tools from artificial intelligence can be used to increase the efficiency of image-based view synthesis algorithms. In particular it is shown how machine learning can help to generate point patterns useful for a variety of computer graphics tasks, how path planning can guide image warping, how sparsity-enforcing optimization can lead to significant speedups in interactive distribution effect rendering, and how probabilistic inference can be used to perform real-time 2D-to-3D conversion.