Extracting Sensor Specific Noise Models

Information

• Publication Type: Bachelor Thesis
• Workgroup(s)/Project(s):
  • Real-Time Shape Acquisition with Sensor-Specific Precision
• Date: August 2017
• Date (Start): 17. June 2016
• Date (End): 28. August 2017
• Matrikelnummer: 01325103
• Note: 1
• First Supervisor: Stefan Ohrhallinger
• Second Supervisor: Michael Wimmer
• Keywords: noise model, surface reconstruction, sensor noise

Abstract

With the growing number of consumer-oriented depth sensors like the Kinect or the newly released Phab2Pro, the question of how precise these sensors are arises. In this thesis we want to evaluate the average noise in the generated depth measurements in both the axial direction and the lateral directions. As part of a two-part project this thesis will view the noise’s development with varying distance and angle. Finally, we will present and evaluate two models describing the noise behavior, with the first being derived from solely this thesis’ measurements and the second one being a combination of the previous model and a model of a colleague. This derived models can be used in a post-processing step to filter the generated depth images.

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BibTeX

@bachelorsthesis{grossmann-2016-baa,
  title =      "Extracting Sensor Specific Noise Models",
  author =     "Nicolas Grossmann",
  year =       "2017",
  abstract =   "With the growing number of consumer-oriented depth sensors
               like the Kinect or the newly released Phab2Pro, the question
               of how precise these sensors are arises. In this thesis we
               want to evaluate the average noise in the generated depth
               measurements in both the axial direction and the lateral
               directions. As part of a two-part project this thesis will
               view the noise’s development with varying distance and
               angle. Finally, we will present and evaluate two models
               describing the noise behavior, with the first being derived
               from solely this thesis’ measurements and the second one
               being a combination of the previous model and a model of a
               colleague. This derived models can be used in a
               post-processing step to filter the generated depth images.",
  month =      aug,
  note =       "1",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  keywords =   "noise model, surface reconstruction, sensor noise",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2017/grossmann-2016-baa/",
}