Browsing by Author "Vázquez, Pere-Pau"

Now showing 1 - 4 of 4
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    Effect of Color Palettes in Heatmaps Perception: a Study
    (The Eurographics Association, 2023) Molina, Elena; Middel, Carolina; Vázquez, Pere-Pau; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
    Heatmaps are a widely used technique in visualization. Unfortunately, they have not been investigated in depth and little is known about the best parameterizations so that they are properly interpreted. The effect of different palettes on our ability to read values is still unknown. To address this issue, we conducted a user study, in which we analyzed the effect of two commonly used color palettes, Blues and Viridis, on value estimation and value search. As a result, we provide some suggestions for what to expect from the heatmap configurations analyzed.
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    Enabling Viewpoint Learning through Dynamic Label Generation
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Schelling, Michael; Hermosilla, Pedro; Vázquez, Pere-Pau; Ropinski, Timo; Mitra, Niloy and Viola, Ivan
    Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpoint qualities suffer from two major drawbacks: dependency on clean surface meshes, which are not always available, and the lack of closed-form expressions, which requires a costly search involving rendering. To overcome these limitations we propose to separate viewpoint selection from rendering through an end-to-end learning approach, whereby we reduce the influence of the mesh quality by predicting viewpoints from unstructured point clouds instead of polygonal meshes. While this makes our approach insensitive to the mesh discretization during evaluation, it only becomes possible when resolving label ambiguities that arise in this context. Therefore, we additionally propose to incorporate the label generation into the training procedure, making the label decision adaptive to the current network predictions. We show how our proposed approach allows for learning viewpoint predictions for models from different object categories and for different viewpoint qualities. Additionally, we show that prediction times are reduced from several minutes to a fraction of a second, as compared to state-of-the-art (SOTA) viewpoint quality evaluation. Code and training data is available at https://github.com/schellmi42/viewpoint_learning, which is to our knowledge the biggest viewpoint quality dataset available.
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    Frontmatter: Eurographics Workshop on Visual Computing for Biology and Medicine 2018
    (The Eurographics Association, 2018) Puig Puig, Anna; Schultz, Thomas; Vilanova, Anna; Hotz, Ingrid; Kozlikova, Barbora; Vázquez, Pere-Pau; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
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    Improving Perception of Molecular Surface Visualizations by Incorporating Translucency Effects
    (The Eurographics Association, 2018) Hermosilla, Pedro; Maisch, Sebastian; Vázquez, Pere-Pau; Ropinski, Timo; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    Molecular surfaces are a commonly used representation in the analysis of molecular structures as they provide a compact description of the space occupied by a molecule and its accessibility. However, due to the high abstraction of the atomic data, fine grain features are hard to identify. Moreover, these representations involve a high degree of occlusions, which prevents the identification of internal features and potentially impacts shape perception. In this paper, we present a set of techniques which are inspired by the properties of translucent materials, that have been developed to improve the perception of molecular surfaces: First, we introduce an interactive algorithm to simulate subsurface scattering for molecular surfaces, in order to improve the thickness perception of the molecule. Second, we present a technique to visualize structures just beneath the surface, by still conveying relevant depth information. And lastly, we introduce reflections and refractions into our visualization that improve the shape perception of molecular surfaces. We evaluate the benefits of these methods through crowd-sourced user studies as well as the feedback from several domain experts.