Deep Terrain Expansion: Terrain Texture Synthesis with Deep Learning

dc.contributor.authorToulatzis, Vasilisen_US
dc.contributor.authorFudos, Ioannisen_US
dc.contributor.editorVidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.en_US
dc.date.accessioned2019-09-11T05:09:05Z
dc.date.available2019-09-11T05:09:05Z
dc.date.issued2019
dc.description.abstractIn real-world applications terrains play a cardinal role in the field of games and geospatial applications such as Geographic Information Systems (GIS). The textures of a terrain are essential for creating virtual photorealistic environments for users. In many cases, the entire texture of a region is not available in high resolution or is much smaller than the required texture to cover a terrain. Tiling of a texture across a terrain or using an enlarged version of it usually fails to provide an acceptable photorealistic result. Consequently, high quality texture synthesis is a central issue in such settings. In this paper, we explore a novel methodology that extends previous work providing both synthesis and expansion/shrinkage of a texture.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20191262
dc.identifier.isbn978-3-03868-096-3
dc.identifier.pages95-96
dc.identifier.urihttps://doi.org/10.2312/cgvc.20191262
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20191262
dc.publisherThe Eurographics Associationen_US
dc.titleDeep Terrain Expansion: Terrain Texture Synthesis with Deep Learningen_US
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