Extracting Noise Models – considering X / Y and Z Noise

Thomas Köppel
Extracting Noise Models – considering X / Y and Z Noise
[thesis]

Information

Abstract

We have developed two different test setups allowing the characterization of noise in X, Y and Z direction for the KinectV2 and the Phab2Pro depth sensors. We have combined these two methods, generating a single noise model allowing a prediction of the amount of noise in specific areas of an image in the three respective directions at a certain distance and rotation. We have conducted two test setups and measured the noise from 900 mm to 3.100 mm for the generation of the noise models. The test setup of this thesis focused on determining the noise in X, Y and Z direction, covering the whole frustum of the respective depth sensor. In this thesis, Z noise was measured against a wall and X and Y noises were measured using a 3D chequerboard that was shifted through the room, allowing the above mentioned coverage of the whole frustum. Along the edges of the cells of the chequerboard, the X and Y noise was measured. The combined model was evaluated by using a solid cube to classify the quality of our noise model.

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Additional images and videos

image: Camera calibration prior to extraction of depth values image: Camera calibration prior to extraction of depth values

Additional files

thesis: Bachelor thesis thesis: Bachelor thesis

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BibTeX

@bachelorsthesis{koeppel-2016-baa,
  title =      "Extracting Noise Models – considering X / Y and Z Noise",
  author =     "Thomas K{"o}ppel",
  year =       "2017",
  abstract =   "We have developed two different test setups allowing the
               characterization of noise in X, Y and Z direction for the
               KinectV2 and the Phab2Pro depth sensors. We have combined
               these two methods, generating a single noise model allowing
               a prediction of the amount of noise in specific areas of an
               image in the three respective directions at a certain
               distance and rotation. We have conducted two test setups and
               measured the noise from 900 mm to 3.100 mm for the
               generation of the noise models. The test setup of this
               thesis focused on determining the noise in X, Y and Z
               direction, covering the whole frustum of the respective
               depth sensor. In this thesis, Z noise was measured against a
               wall and X and Y noises were measured using a 3D
               chequerboard that was shifted through the room, allowing the
               above mentioned coverage of the whole frustum. Along the
               edges of the cells of the chequerboard, the X and Y noise
               was measured. The combined model was evaluated by using a
               solid cube to classify the quality of our noise model.",
  month =      aug,
  note =       "1",
  address =    "Favoritenstrasse 9-11/186, 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/koeppel-2016-baa/",
}