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Daniel R. Rudolf

dblp:71/6553 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

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 architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 100%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing › multi-GPU computing
multi-GPU rendering
0.112007
Flexible Pixel Compositor for Plug-and-Play Multi-Projector Displays · CVPR 2007
Virtual and augmented reality › 3d display › stereoscopic display
autostereoscopic display
0.012007
Flexible Pixel Compositor for Plug-and-Play Multi-Projector Displays · CVPR 2007
GPUs and heterogeneous computing
GPU computing
0.012007
Flexible Pixel Compositor for Plug-and-Play Multi-Projector Displays · CVPR 2007

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

rendering cluster · 0.1pixel compositing · 0.1
YearPublicationVenuePosition
2007 Flexible Pixel Compositor for Plug-and-Play Multi-Projector Displays
abstract
In summary, we are developing the next generation compositor to satisfy the demanding needs from emerging applications. It can be used beyond multi-projector displays. The first is auto-stereoscopic (multi-view) displays, in particular lenticular-based displays. These 3D displays in fact display many views simultaneously and therefore require orders of magnitude more pixels to provide an observer adequate resolution. This can be achieved only by a rendering cluster. Furthermore, images from the rendering nodes typically need to be sliced and interleaved to form the proper composite image for display. We also envision that our flexible hardware can be used for distributed general-purpose computing on graphics processor units (GPGPU). It provides the random write capability missing in most current graphics hardware. By providing a scalable and flexible link among a cluster of GPUs, they can efficiently work in concert to solve problems, both graphical and non-graphical, on a much larger scale.
Ruigang Yang, Daniel R. Rudolf, Vijai Raghunathan
CVPR2