Computer Graphics and Visualization › Publications

2024

Non-parametric analysis of the ensemble of 500 hPa geopotential contour lines
Nicolas F. Chaves-de-Plaza, Mathijs Molenaar, Prerak P. Mody, Marius Staring, René van Egmond, et al.
Depth for Multi-Modal Contour Ensembles
Computer Graphics Forum, 2024
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Workflow and interaction scheme of our Musicon system
Xuejiao Luo, Vera Hoveling, and Elmar Eisemann
Musicon: Glyph-Based Design for Music Visualization and Retrieval
In Proceedings of Vision, Modeling, and Visualization, 2024
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Our Sheared Polygonal Texture Filtering (SPTF) demonstrates superior filtering accuracy compared to hardware- accelerated Anisotropic filtering (ANISO16) at a lower computational cost in comparison to Elliptical Weighted Average (EWA).
Guowei Lu, Jerry Guo, Petr Kellnhofer, and Elmar Eisemann
Sheared Polygonal Texture Filtering
In Proceedings of Graphics Interface, 2024
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A comparison between SVBRDF maps and other state-of-the-art methods
Xuejiao Luo, Leonardo Scandolo, Adrien Bousseau, and Elmar Eisemann
Single-Image SVBRDF Estimation with Learned Gradient Descent
Computer Graphics Forum, 2024
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2023

Illustration of the Image-Space Navigation
Alexander Vieth, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova, and Thomas Höllt
Interactions for Seamlessly Coupled Exploration of High-Dimensional Images and Hierarchical Embeddings
In Proceedings of Vision, Modeling, and Visualization, 2023
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Marcos Pieras, Ricardo Marroquim, Douwe Broekens, Elmar Eisemann, and Anna Vilanova
ComVis-Sail: Comparative Sailing Performance Visualization for Coaching
Computer Graphics Forum, 2023
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Voxelization of the Lumberyard Bistro scene with a 3D brick texture painted on the ground
Mathijs Molenaar and Elmar Eisemann
Editing Compressed High-resolution Voxel Scenes with Attributes
Computer Graphics Forum, 2023
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Top row - Our interactive process. Bottom row - Comparison to the state of the art
Marko Matusovic, Amal Dev Parakkat, and Elmar Eisemann
Interactive Depixelization of Pixel Art through Spring Simulation
Computer Graphics Forum, 2023
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Left. Spectral renderings of the same scene under four standard illuminants. This scene contains RGB textures which have been authored for spectral uplifting with our method. Top-right. Metameric mismatching between two objects, which are constrained to remain identical under D65 and A illuminants. Middle-right. Metamerism on a surface with exaggerated reflectances constrained to the theoretical boundaries of a metamer mismatch volume, such that a message becomes visible only under the FL11 illuminant. Bottom-right. A more realistic example of generated spectral reflectances using our method.
Mark van de Ruit and Elmar Eisemann
Metameric: Spectral Uplifting via Controllable Color Constraints
In Proceedings of SIGGRAPH (Conference Paper Track), 2023
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Overview of our method
Lukas Uzolas, Elmar Eisemann, and Petr Kellnhofer
Template-free Articulated Neural Point Clouds for Reposable View Synthesis
In Proceedings of NeurIPS, 2023
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Terge Väljataga, Kai Pata, Andrea Annus, Michelle Andrade Calisto, Agueda Gomez Cambronero, et al.
Exploring Value and Ethical Dimensions of Disruptive Technologies for Learning and Teaching
In Proceedings of 13th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, mis4TEL, 2023
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Simulation of balloon inflation (Model: Armadillo, 180k vertices).
Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann, and Klaus Hildebrandt
A Fast Geometric Multigrid Method for Curved Surfaces
In Proceedings of SIGGRAPH (Conference Paper Track), 2023
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2022

Scenes used for our tests
Leonardo Scandolo and Elmar Eisemann
Directed acyclic graph encoding for compressed shadow maps
In Proceedings of Conference on High-Performance Graphics, 2022
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Thomas Kroes, Thomas Höllt, Elmar Eisemann, Matthias Alfeld, Francesca Gabrieli, et al.
Visual Analysis of RIS Data for Endmember Selection
In Proceedings of Eurographics Workshop on Graphics and Cultural Heritage, 2022
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Segmentation results when using DV3X, a fully connected multilayer perceptron (ANN), and the method proposed by Biermann et al. (2020) based on the floating debris index (FDI). Both the ANN and the Biermann et al. approaches achieve significantly less accurate results than DV3X.
Àlex S. Gómez, Leonardo Scandolo, and Elmar Eisemann
A learning approach for river debris detection
Int J Appl Earth Obs Geoinformation, 2022
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Mosaic image of the ground truth aligned digitisations.
Jules van der Toorn, Ruben Wiersma, Abbie Vandivere, Ricardo Marroquim, and Elmar Eisemann
A New Baseline for Feature Description on Multimodal Imaging of Paintings
In Proceedings of GCH, 2022
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(left) An image domain, (middle) Hough Transform, (right) Gaussian Sphere
Yancong Lin, Ruben Wiersma, Silvia-Laura Pintea, Klaus Hildebrandt, Elmar Eisemann, and Jan van Gemert
Deep vanishing point detection: Geometric priors make dataset variations vanish
In Proceedings of CVPR, 2022
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Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann, and Klaus Hildebrandt
DeltaConv: anisotropic operators for geometric deep learning on point clouds
ACM Transactions on Graphics, 2022
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Cehao Yu, Maarten Wijntjes, Elmar Eisemann, and Sylvia Pont
Disentangling object color from illuminant color: The role of color shifts
Journal of Vision, 2022
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Incorporating image texture information into dimensionality reduction
Alexander Vieth, Anna Vilanova, Boudewijn P. F. Lelieveldt, Elmar Eisemann, and Thomas Höllt
Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images
In Proceedings of PacificVis, 2022
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