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  Artificial Intelligence for Efficient Image-based View Synthesis

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

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-A589-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-A5B2-8
Genre: Thesis

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 Creators:
Leimkühler, Thomas1, 2, Author              
Seidel, Hans-Peter1, Advisor              
Ritschel, Tobias1, Referee              
Lensch, Hendrik1, Referee              
Drettakis, George3, Referee
Affiliations:
1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, Campus E1 4, 66123 Saarbrücken, DE, ou_1116551              
3External Organizations, ou_persistent22              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 2019-06-242019-08-202019
 Publication Status: Published in print
 Pages: 136 p.
 Publishing info: Saarbrücken : Universität des Saarlandes
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Leimphd2019
DOI: 10.22028/D291-28379
 Degree: PhD

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