Artificial Intelligence for Efficient Image-based View Synthesis
| dc.contributor.author | Leimkühler, Thomas | |
| dc.date.accessioned | 2019-09-20T07:58:45Z | |
| dc.date.available | 2019-09-20T07:58:45Z | |
| dc.date.issued | 2019-06-24 | |
| dc.description.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. | en_US |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/2632810 | |
| dc.language.iso | en | en_US |
| dc.title | Artificial Intelligence for Efficient Image-based View Synthesis | en_US |
| dc.type | Thesis | en_US |
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