J
Julian Kratt
Researcher at University of Konstanz
Publications - 10
Citations - 275
Julian Kratt is an academic researcher from University of Konstanz. The author has contributed to research in topics: Tree (data structure) & Tree structure. The author has an hindex of 4, co-authored 10 publications receiving 237 citations.
Papers
More filters
Journal ArticleDOI
Inverse Procedural Modelling of Trees
TL;DR: This work proposes an inverse modelling approach for stochastic trees that takes polygonal tree models as input and estimates the parameters of a procedural model so that it produces trees similar to the input.
Journal ArticleDOI
Plastic trees: interactive self-adapting botanical tree models
Sören Pirk,Ondrej Stava,Julian Kratt,Michel Abdul Massih Said,Boris Neubert,Radomír Měch,Bedrich Benes,Oliver Deussen +7 more
TL;DR: This work presents a dynamic tree modeling and representation technique that allows complex tree models to interact with their environment and enables content creators to directly interact with trees and to create visually convincing ecosystems interactively.
Proceedings ArticleDOI
Depth-aware coherent line drawings
TL;DR: The proposed algorithm works in real-time and enables users to manipulate the parameter space through instant visual feedback, improving depth perception and object differentiation in large and spatially complex scenes.
Journal ArticleDOI
Woodification: User-Controlled Cambial Growth Modeling
Julian Kratt,Marc Spicker,Alejandro Guayaquil,Marek Fiser,Sören Pirk,Oliver Deussen,John Hart,Bedrich Benes +7 more
TL;DR: This work extends the deformable simplicial complex (DSC) with temporally coherent texturing, and surface cracking with a user‐controllable biological model coupled to the stresses introduced by the cambial growth model.
Journal ArticleDOI
Sketching in Gestalt Space : Interactive Shape Abstraction through Perceptual Reasoning
Julian Kratt,Till Niese,Ruizhen Hu,Hui Huang,Sören Pirk,Andrei Sharf,Daniel Cohen-Or,Oliver Deussen +7 more
TL;DR: An interactive method that allows users to easily abstract complex 3D models with only a few strokes to employ well‐known Gestalt principles to help generalizing user inputs into a full model abstraction while accounting for form, perceptual patterns and semantics of the model.