Single‐Shot Example Terrain Sketching by Graph Neural Networks

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dc.contributor.authorLiu, Y.en_US
dc.contributor.authorBenes, B.en_US
dc.date.accessioned2025-03-07T16:49:03Z
dc.date.available2025-03-07T16:49:03Z
dc.date.issued2025
dc.description.abstractTerrain generation is a challenging problem. Procedural modelling methods lack control, while machine learning methods often need large training datasets and struggle to preserve the topology information. We propose a method that generates a new terrain from a single image for training and a simple user sketch. Our single‐shot method preserves the sketch topology while generating diversified results. Our method is based on a graph neural network (GNN) and builds a detailed relation among the sketch‐extracted features, that is, ridges and valleys and their neighbouring area. By disentangling the influence from different sketches, our model generates visually realistic terrains following the user sketch while preserving the features from the real terrains. Experiments are conducted to show both qualitative and quantitative comparisons. The structural similarity index measure of our generated and real terrains is around 0.8 on average.en_US
dc.description.number1
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.15281
dc.identifier.issn1467-8659
dc.identifier.pages17
dc.identifier.urihttps://doi.org/10.1111/cgf.15281
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15281
dc.publisherEurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectgeometric modellings
dc.subjectmodelling
dc.subjectnatural phenomena
dc.subject• Computing methodologies → Shape modelling; Machine learning algorithms
dc.titleSingle‐Shot Example Terrain Sketching by Graph Neural Networksen_US
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