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Norbert Pfeifer
Researcher at Vienna University of Technology
Publications - 281
Citations - 10086
Norbert Pfeifer is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Point cloud & Lidar. The author has an hindex of 49, co-authored 249 publications receiving 8855 citations. Previous affiliations of Norbert Pfeifer include University of Vienna & University of Innsbruck.
Papers
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Quantification of Overnight Movement of Birch (Betula pendula) Branches and Foliage with Short Interval Terrestrial Laser Scanning
Eetu Puttonen,Christian Briese,Gottfried Mandlburger,Martin Wieser,Martin Pfennigbauer,András Zlinszky,Norbert Pfeifer +6 more
TL;DR: The results indicated that height deciles of the Finnish birch crown had vertical movements between -10.0 and 5.0 cm compared to the situation at sunset, which demonstrates the potential of terrestrial laser scanning measurements in support of chronobiology.
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Improved topographic models via concurrent airborne lidar anddense image matching
TL;DR: It is demonstrated that systematic effects in the resulting scanned and matched 3D point clouds can be minimized based on a hybrid orientation procedure, and improved digital surface models can be derived combining the higher density of the matching point cloud and the higher reliability of LiDAR point clouds, especially in the narrow alleys and courtyards of the study site, a medieval city.
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On-the-job detection and correction of systematic cyclic distance measurement errors of terrestrial laser scanners
TL;DR: By the introduction of on-the-job correction procedures as the one proposed, the systematic errors of TLS can be decreased significantly, making them more suitable to be used as an alternative to single point measurement devices.
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Classification of ALS Point Clouds Using End-to-End Deep Learning
Lukas Winiwarter,Lukas Winiwarter,Gottfried Mandlburger,Gottfried Mandlburger,Stefan Schmohl,Norbert Pfeifer +5 more
TL;DR: It is concluded that the method of the end-to-end system, allowing training on a big variety of classification problems without the need for expert knowledge about neighbourhood features can also successfully be applied to single-point-based classification of ALS point clouds.
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Design and evaluation of a full-wave surface and bottom-detection algorithm for LiDAR bathymetry of very shallow waters
Roland Schwarz,Gottfried Mandlburger,Gottfried Mandlburger,Martin Pfennigbauer,Norbert Pfeifer +4 more
TL;DR: In this article, the accuracy of the algorithm is cross validated against reference measurements obtained from terrestrial survey with a total station and shows negligible bias and virtually no depth dependence for the experimental dataset.