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Valentin Bruder

M. Sc.

Visualization Research Center (VISUS)

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Publications:

2020
1. Frieß, F. ; Braun, M. ; Bruder, V. ; Frey, S. ; Reina, G. ; Ertl, T.: Foveated Encoding for Large High-Resolution Displays. In: IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Visualization and Computer Graphics. (2020), S. 1–10
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  @article{9224182, abstract = {Collaborative exploration of scientific data sets across large high-resolution displays requires both high visual detail as wellas low-latency transfer of image data (oftentimes inducing the need to trade one for the other). In this work, we present a system thatdynamically adapts the encoding quality in such systems in a way that reduces the required bandwidth without impacting the detailsperceived by one or more observers. Humans perceive sharp, colourful details, in the small foveal region around the centre of the fieldof view, while information in the periphery is perceived blurred and colourless. We account for this by tracking the gaze of observers,and respectively adapting the quality parameter of each macroblock used by the H.264 encoder, considering the so-called visual acuityfall-off. This allows to substantially reduce the required bandwidth with barely noticeable changes in visual quality, which is crucial forcollaborative analysis across display walls at different locations. We demonstrate the reduced overall required bandwidth and the highquality inside the foveated regions using particle rendering and parallel coordinate}, author = {Frieß, F. and Braun, M. and Bruder, V. and Frey, S. and Reina, G. and Ertl, T.}, doi = {10.1109/TVCG.2020.3030445}, journal = {IEEE Transactions on Visualization and Computer Graphics}, pages = {1-10}, title = {Foveated Encoding for Large High-Resolution Displays}, url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9224182}, year = 2020 }
  Link
  https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9224182
2. Bruder, Valentin ; Müller, Christoph ; Frey, Steffen ; Ertl, Thomas: On Evaluating Runtime Performance of Interactive Visualizations. In: IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Visualization and Computer Graphics. Bd. 26 (2020), S. 2848–2862
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  @article{vtoappearevaluating, abstract = {As our field matures, evaluation of visualization techniques has extended from reporting runtime performance to studying user behavior. Consequently, many methodologies and best practices for user studies have evolved. While maintaining interactivity continues to be crucial for the exploration of large data sets, no similar methodological foundation for evaluating runtime performance has been developed. Our analysis of 50 recent visualization papers on new or improved techniques for rendering volumes or particles indicates that only a very limited set of parameters like different data sets, camera paths, viewport sizes, and GPUs are investigated, which make comparison with other techniques or generalization to other parameter ranges at least questionable. To derive a deeper understanding of qualitative runtime behavior and quantitative parameter dependencies, we developed a framework for the most exhaustive performance evaluation of volume and particle visualization techniques that we are aware of, including millions of measurements on ten different GPUs. This paper reports on our insights from statistical analysis of this data discussing independent and linear parameter behavior and non-obvious effects. We give recommendations for best practices when evaluating runtime performance of scientific visualization applications, which can serve as a starting point for more elaborate models of performance quantification.}, author = {Bruder, Valentin and Müller, Christoph and Frey, Steffen and Ertl, Thomas}, doi = {10.1109/TVCG.2019.2898435}, journal = {IEEE Transactions on Visualization and Computer Graphics}, month = {09}, pages = {2848-2862}, title = {On Evaluating Runtime Performance of Interactive Visualizations}, url = {https://ieeexplore.ieee.org/document/8637795}, volume = 26, year = 2020 }
  Link
  https://ieeexplore.ieee.org/document/8637795
2019
1. Bruder, Valentin ; Schulz, Christoph ; Bauer, Ruben ; Frey, Steffen ; Weiskopf, Daniel ; Ertl, Thomas: Voronoi-Based Foveated Volume Rendering. In: Johansson, J. ; Sadlo, F. ; Marai, G. E. (Hrsg.) ; Johansson, J. ; Sadlo, F. ; Marai, G. E. (Hrsg.): Proceedings of the Eurographics Conference on Visualization - Short Papers (EuroVis), Proceedings of the Eurographics Conference on Visualization - Short Papers (EuroVis) : Eurographics Association, 2019 — ISBN 978-3-03868-090-1, S. 67–71
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  @inproceedings{conf/vissym/BruderSBFWE19, abstract = {Foveal vision is located in the center of the field of view with a rich impression of detail and color, whereas peripheral visionoccurs on the side with more fuzzy and colorless perception. This visual acuity fall-off can be used to achieve higher frame ratesby adapting rendering quality to the human visual system. Volume raycasting has unique characteristics, preventing a directtransfer of many traditional foveated rendering techniques. We present an approach that utilizes the visual acuity fall-off toaccelerate volume rendering based on Linde-Buzo-Gray sampling and natural neighbor interpolation. First, we measure gazeusing a stationary 1200 Hz eye-tracking system. Then, we adapt our sampling and reconstruction strategy to that gaze. Finally,we apply a temporal smoothing filter to attenuate undersampling artifacts since peripheral vision is particularly sensitive tocontrast changes and movement. Our approach substantially improves rendering performance with barely perceptible changes invisual quality. We demonstrate the usefulness of our approach through performance measurements on various data se}, author = {Bruder, Valentin and Schulz, Christoph and Bauer, Ruben and Frey, Steffen and Weiskopf, Daniel and Ertl, Thomas}, booktitle = {Proceedings of the Eurographics Conference on Visualization - Short Papers (EuroVis)}, doi = {10.2312/evs.20191172}, editor = {Johansson, Jimmy and Sadlo, Filip and Marai, G. Elisabeta}, ee = {https://doi.org/10.2312/evs.20191172}, isbn = {978-3-03868-090-1}, pages = {67-71}, publisher = {Eurographics Association}, title = {Voronoi-Based Foveated Volume Rendering}, url = {https://doi.org/10.2312/evs.20191172}, year = 2019 }
  Link
  https://doi.org/10.2312/evs.20191172
2. Bruder, Valentin ; Kurzhals, Kuno ; Frey, Steffen ; Weiskopf, Daniel ; Ertl, Thomas: Space-Time Volume Visualization of Gaze and Stimulus. In: Krejtz, K. ; Sharif, B. (Hrsg.) ; Krejtz, K. ; Sharif, B. (Hrsg.): Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA) : ACM, 2019 — ISBN 978-1-4503-6709-7, S. 12:1-12:9
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  @inproceedings{conf/etra/BruderKFWE19, abstract = {We present a method for the spatio-temporal analysis of gaze data from multiple participants in the context of a video stimulus. For such data, an overview of the recorded patterns is important to identify common viewing behavior (such as attentional synchrony) and outliers. We adopt the approach of space-time cube visualization, which extends the spatial dimensions of the stimulus by time as the third dimension. Previous work mainly handled eye tracking data in the space-time cube as point cloud, providing no information about the stimulus context. This paper presents a novel visualization technique that combines gaze data, a dynamic stimulus, and optical flow with volume rendering to derive an overview of the data with contextual information. With specifically designed transfer functions, we emphasize different data aspects, making the visualization suitable for explorative analysis and for illustrative support of statistical findings alike.}, author = {Bruder, Valentin and Kurzhals, Kuno and Frey, Steffen and Weiskopf, Daniel and Ertl, Thomas}, booktitle = {Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA)}, doi = {10.1145/3314111.3319812}, editor = {Krejtz, Krzysztof and Sharif, Bonita}, ee = {https://doi.org/10.1145/3314111.3319812}, isbn = {978-1-4503-6709-7}, pages = {12:1-12:9}, publisher = {ACM}, title = {Space-Time Volume Visualization of Gaze and Stimulus}, url = {https://doi.org/10.1145/3314111.3319812}, year = 2019 }
  Link
  https://doi.org/10.1145/3314111.3319812
3. Bruder, Valentin ; Lahmar, Houssem Ben ; Hlawatsch, Marcel ; Frey, Steffen ; Burch, Michael ; Weiskopf, Daniel ; Herschel, Melanie ; Ertl, Thomas: Volume-Based Large Dynamic Graph Analysis Supported by Evolution Provenance. In: Multimedia Tools and Applications, Multimedia Tools and Applications. Bd. 78 (2019), Nr. 23, S. 32939–32965
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  @article{journals/mta/BruderLHFBWHE19, abstract = {We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.}, author = {Bruder, Valentin and Lahmar, Houssem Ben and Hlawatsch, Marcel and Frey, Steffen and Burch, Michael and Weiskopf, Daniel and Herschel, Melanie and Ertl, Thomas}, doi = {10.1007/s11042-019-07878-6}, ee = {https://doi.org/10.1007/s11042-019-07878-6}, journal = {Multimedia Tools and Applications}, number = 23, pages = {32939-32965}, title = {Volume-Based Large Dynamic Graph Analysis Supported by Evolution Provenance}, url = {https://doi.org/10.1007/s11042-019-07878-6}, volume = 78, year = 2019 }
  Link
  https://doi.org/10.1007/s11042-019-07878-6
2018
1. Frieß, Florian ; Landwehr, Mathias ; Bruder, Valentin ; Frey, Steffen ; Ertl, Thomas: Adaptive Encoder Settings for Interactive Remote Visualisation on High-Resolution Displays. In: Proceedings of the IEEE Symposium on Large Data Analysis and Visualization - Short Papers (LDAV), Proceedings of the IEEE Symposium on Large Data Analysis and Visualization - Short Papers (LDAV) : IEEE, 2018 — ISBN 978-1-5386-6873-3, S. 87–91
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  @inproceedings{conf/ldav/FriessLBFE18, abstract = {We present an approach that dynamically adapts encoder settings for image tiles to yield the best possible quality for a given bandwidth. This reduces the overall size of the image while preserving details. Our application determines the encoding settings in two steps. In the first step, we predict the quality and size of the tiles for different encoding settings using a convolutional neural network. In the second step, we assign the optimal encoder setting to each tile, so that the overall size of the image is lower than a predetermined threshold. Commonly, for tiles that contain complicated structures, a high quality setting is used in order to prevent major information loss, while quality settings are lowered for others to keep the size below the threshold. We demonstrate that we can reduce the overall size of the image while preserving the details in areas of interest using the example of both particle and volume visualisation applications.}, author = {Frieß, Florian and Landwehr, Mathias and Bruder, Valentin and Frey, Steffen and Ertl, Thomas}, booktitle = {Proceedings of the IEEE Symposium on Large Data Analysis and Visualization - Short Papers (LDAV)}, doi = {10.1109/LDAV.2018.8739215}, ee = {https://doi.org/10.1109/LDAV.2018.8739215}, isbn = {978-1-5386-6873-3}, pages = {87-91}, publisher = {IEEE}, title = {Adaptive Encoder Settings for Interactive Remote Visualisation on High-Resolution Displays}, url = {https://ieeexplore.ieee.org/document/8739215}, year = 2018 }
  Link
  https://ieeexplore.ieee.org/document/8739215
2. Bruder, Valentin ; Hlawatsch, Marcel ; Frey, Steffen ; Burch, Michael ; Weiskopf, Daniel ; Ertl, Thomas: Volume-Based Large Dynamic Graph Analytics. In: Banissi, E. ; Francese, R. ; Bannatyne, M. W. McK. ; Wyeld, T. G. ; Sarfraz, M. ; Pires, J. M. ; Ursyn, A. ; Bouali, F. ; u. a. (Hrsg.) ; Banissi, E. ; Francese, R. ; Bannatyne, M. W. McK. ; Wyeld, T. G. ; Sarfraz, M. ; Pires, J. M. ; Ursyn, A. ; Bouali, F. ; u. a. (Hrsg.): Proceedings of the International Conference Information Visualisation (IV), Proceedings of the International Conference Information Visualisation (IV) : IEEE, 2018 — ISBN 978-1-5386-7202-0, S. 210–219
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  @inproceedings{conf/iv/BruderHFBWE18, abstract = {We present an approach for interactively analyzing large dynamic graphs consisting of several thousand time steps with a particular focus on temporal aspects. we employ a static representation of the time-varying graph based on the concept of space-time cubes, i.e., we create a volumetric representation of the graph by stacking the adjacency matrices of each of its time steps. To achieve an efficient analysis of complex data, we discuss three classes of analytics methods of particular importance in this context: data views, aggregation and filtering, and comparison. For these classes, we present a GPU-based implementation of respective analysis methods that enable the interactive analysis of large graphs. We demonstrate the utility as well as the scalability of our approach by presenting application examples for analyzing different time-varying data sets.}, author = {Bruder, Valentin and Hlawatsch, Marcel and Frey, Steffen and Burch, Michael and Weiskopf, Daniel and Ertl, Thomas}, booktitle = {Proceedings of the International Conference Information Visualisation (IV)}, crossref = {conf/iv/2018}, doi = {10.1109/iV.2018.00045}, editor = {Banissi, Ebad and Francese, Rita and Bannatyne, Mark W. McK. and Wyeld, Theodor G. and Sarfraz, Muhammad and Pires, João Moura and Ursyn, Anna and Bouali, Fatma and Datia, Nuno and Venturini, Gilles and Polese, Giuseppe and Deufemia, Vincenzo and Mascio, Tania Di and Temperini, Marco and Sciarrone, Filippo and Malandrino, Delfina and Zaccagnino, Rocco and Díaz, Paloma and Papadopoulo, Fragkiskos and Anta, Antonio Fernández and Cuzzocrea, Alfredo and Risi, Michele and Erra, Ugo and Rossano, Veronica}, ee = {http://doi.ieeecomputersociety.org/10.1109/iV.2018.00045}, isbn = {978-1-5386-7202-0}, pages = {210-219}, publisher = {IEEE}, title = {Volume-Based Large Dynamic Graph Analytics}, url = {https://ieeexplore.ieee.org/document/8564163}, year = 2018 }
  Link
  https://ieeexplore.ieee.org/document/8564163
2017
1. Tkachev, Gleb ; Frey, Steffen ; Müller, Christoph ; Bruder, Valentin ; Ertl, Thomas: Prediction of Distributed Volume Visualization Performance to Support Render Hardware Acquisition. In: Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (EGPGV) : Eurographics Association, 2017, S. 11–20
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  @inproceedings{tkachev2017prediction, abstract = {We present our data-driven, neural network-based approach to predicting the performance of a distributed GPU volume renderer for supporting cluster equipment acquisition. On the basis of timing measurements from a single cluster as well as from individual GPUs, we are able to predict the performance gain of upgrading an existing cluster with additional or faster GPUs, or even purchasing of a new cluster with a comparable network configuration. To achieve this, we employ neural networks to capture complex performance characteristics. However, merely relying on them for the prediction would require the collection of training data on multiple clusters with different hardware, which is impractical in most cases. Therefore, we propose a two-level approach to prediction, distinguishing between node and cluster level. On the node level, we generate performance histograms on individual nodes to capture local rendering performance. These performance histograms are then used to emulate the performance of different rendering hardware for cluster-level measurement runs. Crucially, this variety allows the neural network to capture the compositing performance of a cluster separately from the rendering performance on individual nodes. Therefore, we just need a performance histogram of the GPU of interest to generate a prediction. We demonstrate the utility of our approach using different cluster configurations as well as a range of image and volume resolutions.}, author = {Tkachev, Gleb and Frey, Steffen and Müller, Christoph and Bruder, Valentin and Ertl, Thomas}, doi = {10.2312/pgv.20171089}, journal = {Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (EGPGV)}, pages = {11-20}, publisher = {Eurographics Association}, title = {Prediction of Distributed Volume Visualization Performance to Support Render Hardware Acquisition}, url = {http://dx.doi.org/10.2312/pgv.20171089}, year = 2017 }
  Link
  http://dx.doi.org/10.2312/pgv.20171089
2. Heinemann, Moritz ; Bruder, Valentin ; Frey, Steffen ; Ertl, Thomas: Power Efficiency of Volume Raycasting on Mobile Devices. In: Association, T. E. (Hrsg.) ; Association, T. E. (Hrsg.): EuroVis 2017 - Posters, EuroVis 2017 - Posters, 2017. — Rezensiertes Poster
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  @inproceedings{94958fee8e0d4fe1990a81bc44135d7a, author = {Heinemann, Moritz and Bruder, Valentin and Frey, Steffen and Ertl, Thomas}, booktitle = {EuroVis 2017 - Posters}, editor = {Association, The Eurographics}, note = {Rezensiertes Poster}, privnote = {uid:2394}, title = {Power Efficiency of Volume Raycasting on Mobile Devices}, url = {http://dx.doi.org/10.2312/eurp.20171166}, year = 2017 }
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  http://dx.doi.org/10.2312/eurp.20171166
3. Bruder, Valentin ; Frey, Steffen ; Ertl, Thomas: Prediction-Based Load Balancing and Resolution Tuning for Interactive Volume Raycasting. In: Visual Informatics, Visual Informatics. Bd. 1 (2017), Nr. 2, S. 106–117
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  @article{journals/vi/BruderFE17, abstract = {We present an integrated approach for real-time performance prediction of volume raycasting that we employ for load balancing and sampling resolution tuning. In volume rendering, the usage of acceleration techniques such as empty space skipping and early ray termination, among others, can cause significant variations in rendering performance when users adjust the camera configuration or transfer function. These variations in rendering times may result in unpleasant effects such as jerky motions or abruptly reduced responsiveness during interactive exploration. To avoid those effects, we propose an integrated approach to adapt rendering parameters according to performance needs. We assess performance-relevant data on-the-fly, for which we propose a novel technique to estimate the impact of early ray termination. On the basis of this data, we introduce a hybrid model, to achieve accurate predictions with minimal computational footprint. Our hybrid model incorporates aspects from analytical performance modeling and machine learning, with the goal to combine their respective strengths. We show the applicability of our prediction model for two different use cases: (1) to dynamically steer the sampling density in object and/or image space and (2) to dynamically distribute the workload among several different parallel computing devices. Our approach allows to reliably meet performance requirements such as a user-defined frame rate, even in the case of sudden large changes to the transfer function or the camera orientation.}, author = {Bruder, Valentin and Frey, Steffen and Ertl, Thomas}, doi = {10.1016/j.visinf.2017.09.001}, ee = {https://doi.org/10.1016/j.visinf.2017.09.001}, journal = {Visual Informatics}, number = 2, pages = {106-117}, title = {Prediction-Based Load Balancing and Resolution Tuning for Interactive Volume Raycasting}, url = {https://doi.org/10.1016/j.visinf.2017.09.001}, volume = 1, year = 2017 }
  Link
  https://doi.org/10.1016/j.visinf.2017.09.001
2016
1. Bruder, Valentin ; Frey, Steffen ; Ertl, Thomas: Real-Time Performance Prediction and Tuning for Interactive Volume Raycasting. In: ACM (Hrsg.) ; ACM (Hrsg.): SIGGRAPH ASIA 2016 Symposium on Visualization, SIGGRAPH ASIA 2016 Symposium on Visualization. Bd. 2016 : ACM, 2016
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  @inproceedings{4f71f5a1621147b4b5183bfdb90e10f0, author = {Bruder, Valentin and Frey, Steffen and Ertl, Thomas}, booktitle = {SIGGRAPH ASIA 2016 Symposium on Visualization}, editor = {ACM}, number = 7, privnote = {uid:2336}, publisher = {ACM}, title = {Real-Time Performance Prediction and Tuning for Interactive Volume Raycasting}, url = {http://dx.doi.org/10.1145/3002151.3002156}, volume = 2016, year = 2016 }
  Link
  http://dx.doi.org/10.1145/3002151.3002156

Valentin Bruder

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2019

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Valentin Bruder

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2020

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2019

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2018

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2017

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2016

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