Computer Graphics and Visualization › Publications

2018

Leonardo Torok, Elmar Eisemann, Daniela Trevisan, Anselmo Montenegro, and Esteban Clua
PadCorrect: Correcting User Input on a Virtual Gamepad
In Proceedings of Graphics Interface, 2018
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Nicola Pezzotti, Jean-Daniel Fekete, Thomas Höllt, Boudewijn P. F. Lelieveldt, Elmar Eisemann, and Anna Vilanova
Multiscale Visualization and Exploration of Large Bipartite Graphs
Computer Graphics Forum, 2018
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2017

Occlusion-aware cutaway generation. From left to right: User-drawn curve, selected region (shaded green), cutout revealing interior, final illustration using consecutive cutaways.
Mohamed Radwan, Stefan Ohrhallinger, Elmar Eisemann, and Michael Wimmer
Cut and Paint: Occlusion-Aware Subset Selection for Surface Processing
In Proceedings of Graphics Interface, 2017
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Rodrigo Baravalle, Leonardo Scandolo, Claudio Delrieux, Cristian García Bauza, and Elmar Eisemann
Realistic modeling of porous materials
Comput Animat Virtual Worlds, 2017
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Embeddings of the MNIST dataset using different approximation levels
Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Laurens van der Maaten, Thomas Höllt, Elmar Eisemann, and Anna Vilanova
Approximated and User Steerable tSNE for Progressive Visual Analytics
IEEE Transactions on Visualization and Computer Graphics, 2017
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Retinal photoreceptor distribution. Image adapted from Goldstein
Martin Weier, Michael Stengel, Thorsten Roth, Piotr Didyk, Elmar Eisemann, et al.
Perception-driven Accelerated Rendering
Computer Graphics Forum, 2017
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Analysis of the CD4+ T-cell compartment in inflammatory intestinal diseases. a Third HSNE level embedding of the CD4+ T cells (1.4 × 106 cells, selected in Fig. 3). Color and size of landmarks as described in Fig. 3. Right panel shows density features for the level 3 embedding. Blue encirclement indicates selection of landmarks representing CD28−CD4+ T cells. b Embedding of the CD28−CD4+ T cells (2.6 × 104 cells) at single-cell resolution. Bottom-left panel shows yellow and black dashed encirclements based on CD56− and CD56+ expression, respectively. Three bottom-right panels show cells colored according to: (left) from subjects with different disease status (CeD, Crohn, EATLII, RCDII, and controls), (middle) sampling status (annotated subset, discarded by ACCENSE and downsampled) and (right) tissue-of-origin (blood and intestine)
Vincent van Unen, Thomas Höllt, Nicola Pezzotti, Na Li, Marcel Reinders, et al.
Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
Nat Commun, 2017
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Cloud reflection for a flood in the city of Rotterdam due to a hypothetical scenario of heavy rainfall (100 mm/h)
Johannes G. Leskens, Christian Kehl, Tim Tutenel, Timothy R. Kol, Gerwin de Haan, et al.
An interactive simulation and visualization tool for flood analysis usable for practitioners
Mitig Adapt Strateg Glob Chang, 2017
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: (Top) Virtual environment set-up for static scenario as seen by participants. (Bottom) Insets of exemplary stimulus on the 3rd right sphere, shown for white (left) and black (right) interpolation (effect exaggerated for depiction).
Steve Grogorick, Michael Stengel, Elmar Eisemann, and Marcus Magnor
Subtle gaze guidance for immersive environments
In Proceedings of SAP, 2017
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Split-depth frames over time generated by our approach. Via an occlusion cue, split-depth images can induce a 3D effect
Jingtang Liao, Martin Eisemann, and Elmar Eisemann
Split-Depth Image Generation and Optimization
Computer Graphics Forum, 2017
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Fur design on the bunny mesh. Left: constraints and resulting tangential vector field spline, right: output field visualized as fur on the bunny
Christopher Brandt, Leonardo Scandolo, Elmar Eisemann, and Klaus Hildebrandt
Spectral Processing of Tangential Vector Fields
Computer Graphics Forum, 2017
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: Three dODF-based glyphs for (a) an ensemble with gradually varying shape and orientation and two ensembles of linear tensors with crossing angle of (b) 60 ° and (c) 45 °, respectively. The variation threshold is set to 60% of the maximum variation. The ensembles are illustrated by the small black icons on top.
Changgong Zhang, Matthan Caan, Thomas Höllt, Elmar Eisemann, and Anna Vilanova
Overview + Detail Visualization for Ensembles of Diffusion Tensors
Computer Graphics Forum, 2017
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We compare our method to conventional and gradient-domain path tracing in an equal-time comparison. Gradient-domain path reusing produces visually pleasant images with much less noise than path tracing and significantly lower artifacts than gradient-domain path tracing given the same time.
Pablo Bauszat, Victor Petitjean, and Elmar Eisemann
Gradient-domain path reusing
ACM Transactions on Graphics, 2017
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The 95% confidence interval highlighted in green for the t-distribution with 4 degrees of freedom (N = 5, N = 3 and α = 0.05). The left most image shows the two-sided confidence interval, while the middle and right image show the one-sided confidence intervals. The areas highlighted in red fall outside the interval.
Niels de Hoon, Elmar Eisemann, and Anna Vilanova
From a User Study to a Valid Claim: How to Test Your Hypothesis and Avoid Common Pitfalls
In Proceedings of EuroRV³@EuroVis, 2017
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Example of our stylized scattering. Left: physically correct single scattering using the original occluders. The leaves of the tree block most of the light, causing a rather subtle effect. Right: stylized scattering with occluder manipulation. Using our system, an artist can easily add holes into the shadow map of the tree, producing more pronounced scattering effects. While physically incorrect, it is not obvious for the viewer that the right image uses fake occlusion information. Surface shadows are created from the original shadow map.
Timothy R. Kol, Oliver Klehm, Hans-Peter Seidel, and Elmar Eisemann
Expressive Single Scattering for Light Shaft Stylization
IEEE Transactions on Visualization and Computer Graphics, 2017
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Depth estimation from scribbles. Scribble input (left), only using the scribble input results in a smooth depth map lacking discontinuities (middle), by involving the input image gradients, the depth propagation is improved (right). (Image source: Wikimedia Commons)
Jingtang Liao, Shuheng Shen, and Elmar Eisemann
Depth Map Design and Depth-based Effects With a Single Image
In Proceedings of Graphics Interface, 2017
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Light Paintings created with our approach. The production only took minutes, design and modifications are performed in real time.
Nestor Z. Salamon, Marcel Lancelle, and Elmar Eisemann
Computational Light Painting Using a Virtual Exposure
Computer Graphics Forum, 2017
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Philipp von Radziewsky, Thomas Kroes, Martin Eisemann, and Elmar Eisemann
Efficient Stochastic Rendering of Static and Animated Volumes Using Visibility Sweeps
IEEE Transactions on Visualization and Computer Graphics, 2017
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De testopstelling (merk op de speciale + en – knoppen, groot en klein voor zoomen).
Radan Suba, Mattijs Driel, Martijn Meijers, Peter van Oosterom, and Elmar Eisemann
First usability tests of vario-scale maps
Geo-Info, 2017
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Comparison (normal map) of our approach with the two state-of-the-art near-light PS algorithms from Ahmad et al. [4] and Mecca et al. [32] for the MONKEY scene. Please note that shadows and discontinuities in our input makes the data already unsuitable for these algorithms, hence it is obvious that their reconstructions fail for most parts.
Jingtang Liao, Bert Buchholz, Jean-Marc Thiery, Pablo Bauszat, and Elmar Eisemann
Indoor Scene Reconstruction Using Near-Light Photometric Stereo
IEEE Trans Image Process, 2017
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