Abstract
In this paper we present a new calibration approach for focused plenoptic cameras. We derive a new mathematical projection model of a focused plenoptic camera which considers lateral as well as depth distortion. Therefore, we derive a new depth distortion model directly from the theory of depth estimation in a focused plenoptic camera. In total the model consists of five intrinsic parameters, the parameters for radial and tangential distortion in the image plane and two new depth distortion parameters. In the proposed calibration we perform a complete bundle adjustment based on a 3D calibration target. The residual of our optimization approach is three dimensional, where the depth residual is defined by a scaled version of the inverse virtual depth difference and thus conforms well to the measured data. Our method is evaluated based on different camera setups and shows good accuracy. For a better characterization of our approach we evaluate the accuracy of virtual image points projected back to 3D space.
Highlights
In the last years light-field cameras became more and more popular
Plenoptic cameras replace for example other depth sensors like stereo camera systems
This paper introduces a new model for focused plenoptic cameras (Lumsdaine and Georgiev, 2009, Perwaß and Wietzke, 2012) and presents how this model can be precisely determined in a metric calibration process using a 3D calibration target
Summary
Different from regular cameras, a 2D image of a scene, but a complete 4D light-field representation (Adelson and Wang, 1992, Gortler et al, 1996). Due to this additional information plenoptic cameras find usage for a variety of applications in photogrammetry as well as computer vision. Plenoptic cameras replace for example other depth sensors like stereo camera systems. To use such a depth sensor for instance in photogrammetric applications a precise metric calibration is mandatory. This paper introduces a new model for focused plenoptic cameras (Lumsdaine and Georgiev, 2009, Perwaß and Wietzke, 2012) and presents how this model can be precisely determined in a metric calibration process using a 3D calibration target
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