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David Steiner 0003

dblp:215/5212 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-6851-4767ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Rendering · 33% Visualization and visual analytics · 32% Image and video processing · 25%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering
parallel rendering
0.412020
Equalizer 2.0-Convergence of a Parallel Rendering Framework · IEEE Trans. Vis. Comput. Graph. 2020
Image and video processing
image filtering
0.312018
Multiresolution Volume Filtering in the Tensor Compressed Domain · IEEE Trans. Vis. Comput. Graph. 2018
Visualization and visual analytics
volume visualization
0.312018
Multiresolution Volume Filtering in the Tensor Compressed Domain · IEEE Trans. Vis. Comput. Graph. 2018
Visualization and visual analytics › scientific visualization
multiscale visualization
0.112018
Multiresolution Volume Filtering in the Tensor Compressed Domain · IEEE Trans. Vis. Comput. Graph. 2018

Methods — techniques the papers use, named apart from their topics

parallel rendering algorithms · 0.9tensor approximation · 0.3multiscale compression · 0.3convolution filtering · 0.3
YearPublicationVenuePosition
2020 Equalizer 2.0-Convergence of a Parallel Rendering Framework
abstract
Developing complex, real world graphics applications which leverage multiple GPUs and computers for interactive 3D rendering tasks is a complex task. It requires expertise in distributed systems and parallel rendering in addition to the application domain itself. We present a mature parallel rendering framework which provides a large set of features, algorithms and system integration for a wide range of real-world research and industry applications. Using the Equalizer parallel rendering framework, we show how a wide set of generic algorithms can be integrated in the framework to help application scalability and development in many different domains, highlighting how concrete applications benefit from the diverse aspects and use cases of Equalizer. We present novel parallel rendering algorithms, powerful abstractions for large visualization setups and virtual reality, as well as new experimental results for parallel rendering and data distribution.
Stefan Eilemann, David Steiner 0003, Renato Pajarola
IEEE Trans. Vis. Comput. Graph.2
2018 Multiresolution Volume Filtering in the Tensor Compressed Domain
abstract
Signal processing and filter operations are important tools for visual data processing and analysis. Due to GPU memory and bandwidth limitations, it is challenging to apply complex filter operators to large-scale volume data interactively. We propose a novel and fast multiscale compression-domain volume filtering approach integrated into an interactive multiresolution volume visualization framework. In our approach, the raw volume data is decomposed offline into a compact hierarchical multiresolution tensor approximation model. We then demonstrate how convolution filter operators can effectively be applied in the compressed tensor approximation domain. To prevent aliasing due to multiresolution filtering, our solution (a) filters accurately at the full spatial volume resolution at a very low cost in the compressed domain, and (b) reconstructs and displays the filtered result at variable level-of-detail. The proposed system is scalable, allowing interactive display and filtering of large volume datasets that may exceed the available GPU memory. The desired filter kernel mask and size can be modified online, producing immediate visual results.
Rafael Ballester-Ripoll, David Steiner 0003, Renato Pajarola
IEEE Trans. Vis. Comput. Graph.2