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Volume 6, issue 1 | Copyright

Special issue: Sensors and Measurement Systems 2016

J. Sens. Sens. Syst., 6, 171-184, 2017
https://doi.org/10.5194/jsss-6-171-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Regular research article 08 May 2017

Regular research article | 08 May 2017

Physically based computer graphics for realistic image formation to simulate optical measurement systems

Max-Gerd Retzlaff1,2, Johannes Hanika1, Jürgen Beyerer2,3, and Carsten Dachsbacher1 Max-Gerd Retzlaff et al.
  • 1Karlsruhe Institute of Technology (KIT), Institute for Visualization and Data Analysis (IVD), Computer Graphics Group, Karlsruhe, Germany
  • 2Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), Karlsruhe, Germany
  • 3Karlsruhe Institute of Technology (KIT), Institute for Anthropomatics and Robotics, Vision and Fusion Laboratory (IES), Karlsruhe, Germany

Abstract. Physically based image synthesis methods, a research direction in computer graphics (CG), are capable of simulating optical measuring systems in their entirety and thus constitute an interesting approach for the development, simulation, optimization, and validation of such systems. In addition, other CG methods, so-called procedural modeling techniques, can be used to quickly generate large sets of virtual samples and scenes thereof that comprise the same variety as physical testing objects and real scenes (e.g., if digitized sample data are not available or difficult to acquire). Appropriate image synthesis (rendering) techniques result in a realistic image formation for the virtual scenes, considering light sources, material, complex lens systems, and sensor properties, and can be used to evaluate and improve complex measuring systems and automated optical inspection (AOI) systems independent of a physical realization. In this paper, we provide an overview of suitable image synthesis methods and their characteristics, we discuss the challenges for the design and specification of a given measuring situation in order to allow for a reliable simulation and validation, and we describe an image generation pipeline suitable for the evaluation and optimization of measuring and AOI systems.

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