Daniel Pustka
Technische Universität München
22 Papers
301 Citations
Daniel Pustka is an academic researcher from Technische Universität München. The author has contributed to research in topics: Augmented reality & Sensor fusion. The author has an hindex of 11, co-authored 22 publications. Previous affiliations of Daniel Pustka include Information Technology University & Ludwig Maximilian University of Munich.
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Papers
Ubiquitous Tracking for Augmented Reality
Joseph Newman,Martin Wagner,Martin Bauer,Asa MacWilliams,Thomas Pintaric,Dagmar Beyer,Daniel Pustka,Franz Strasser,Dieter Schmalstieg,Gudrun Klinker +9 more
- 02 Nov 2004
TL;DR: This paper presents a software implementation, in which a dynamic data flow network of distributed software components is thereby constructed in response to queries and optimisation criteria specified by applications, and is demonstrated using a small laboratory example, and larger setups modelled in a simulation environment.
Using laser projectors for augmented reality
Björn Schwerdtfeger,Daniel Pustka,Andreas Hofhauser,Gudrun Klinker +3 more
- 27 Oct 2008
TL;DR: The development of an Augmented Reality Laser Projector is described and experiences setting up AR systems that use laser projectors are reported on, reasoning about several design criteria.
A System Architecture for Ubiquitous Tracking Environments
Manuel Huber,Daniel Pustka,Peter Keitler,Florian Echtler,Gudrun Klinker +4 more
- 13 Nov 2007
TL;DR: This paper presents a centrally coordinated peer-to-peer architecture for ubiquitous tracking, where a server computes optimal data flow configurations for sensor and application clients, which are directly exchanging tracking data with low latency using a light-weight data flow framework.
Spatial relationship patterns: elements of reusable tracking and calibration systems
Daniel Pustka,Manuel Huber,Martin Bauer,Gudrun Klinker +3 more
- 22 Oct 2006
TL;DR: This paper introduces a formal model for representing such spatial relationship patterns and presents a small catalog of patterns frequently used in augmented reality systems and describes an algorithm to identify patterns in SRGs at runtime for automatic construction of data flows networks for tracking and calibration.
Predicting and estimating the accuracy of n-occular optical tracking systems
Martin Bauer,Michael Schlegel,Daniel Pustka,Nassir Navab,Gudrun Klinker +4 more
- 22 Oct 2006
TL;DR: A model for the prediction of the target registration error (TRE) in these kinds of tracking systems is proposed by estimating the fiducial location error (FLE) from two-dimensional errors on the image plane and propagating that error to a given point of interest.
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