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    Artificial Intelligence for Efficient Image-based View Synthesis

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    Dissertation_Leimkuehler_UdS.pdf (14.08Mb)
    Date
    2019-06-24
    Author
    Leimkühler, Thomas
    Item/paper (currently) not available via TIB Hannover.
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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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    https://diglib.eg.org:443/handle/10.2312/2632810
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