Calibration and Epipolar Geometry of Generic Heterogenous Camera Systems

Abstract

ABSTRACT:The application of perspective camera systems in photogrammetry and computer vision is state of the art. In recent years nonperspective and especially omnidirectional camera systems were increasingly used in close-range photogrammetry tasks. In general perspective camera model, i. e. pinhole model, cannot be applied when using non-perspective camera systems. However, several camera models for different omnidirectional camera systems are proposed in literature. Using different types of cameras in a heterogeneous camera system may lead to an advantageous combination. The advantages of different camera systems, e. g. field of view and resolution, result in a new enhanced camera system. If these different kinds of cameras can be modeled, using a unified camera model, the total calibration process can be simplified. Sometimes it is not possible to give the specific camera model in advance. In these cases a generic approach is helpful. Furthermore, a simple stereo reconstruction becomes possible using a fisheye and a perspective camera for example. In this paper camera models for perspective, wide-angle and omnidirectional camera systems are evaluated. The crucial initialization of the model's parameters is conducted using a generic method that is independent of the particular camera system. The accuracy of this generic camera calibration approach is evaluated by calibration of a dozen of real camera systems. It will be shown, that a unified method of modeling, parameter approximation and calibration of interior and exterior orientation can be applied to derive 3D object data.

Deflectometry for specular surfaces: an overview

“…Another important use-case is non-standard optics (omnidirectional, telecentric, catadioptric, etc.). In precision DM, generic and model-free approaches have been shown to reduce measurement errors even for paraxial imaging, so the topic has attracted a lot of attention (Sturm and Ramalingam, 2004;Grossberg and Nayar, 2005;Wang et al, 2005;Kannala and Brandt, 2006;Barreto et al, 2008;Bothe et al, 2010;Luber, 2010;Ramalingam et al, 2010;Rosebrock and Wahl, 2012;Xiang et al, 2013;Pak, 2016b;Pak, 2016c;Prinzler et al, 2018;Schöps et al, 2020;Gauchan et al, 2021;Uhlig and Heizmann, 2021).…”

Accuracy Evaluation of Stereo Camera Systems With Generic Camera Models

“…One generic projection model has been introduced by Luber and Reulke (Luber and Reulke, 2010), namely polynomial mapping of inclination angle θ to camera chip radius r. Generally any of the above generic radius r to radius r d distortion models can also be utilized as generic inclination angle θ to radius r projection models. In (Luber and Reulke, 2010) and (Luber et al, 2012), different of these models are used as projection models and evaluated. Also, a method of calibrating such models is presented.…”

“…In general the inverse of a projection model has to be determined numerically, as exact solutions are either expensive or analytically not possible. Also note that the calibration of the cameras for this paper has been done in the fashion of (Luber et al, 2012), refer to this paper for the details.…”

3D Browsing of Wide‐Angle Fisheye Images Under View‐Dependent Perspective Correction

“…Due to their generally smaller size and lower price, fisheye cameras nowadays are widely used in mobile phones (Sahin, 2016) and in applications including video surveillance (Kim et al., 2016), unmanned aerial vehicle mapping (Gurtner et al., 2009), automatic parking systems (Li and Hai, 2011) and robotic vision (Delibasis et al., 2015). The main weakness of fisheye cameras is that the fisheye image has serious distortions: aberrations are introduced and resolution losses occur when the image is corrected (Luber et al., 2012). Many correction algorithms have been developed to recover the “natural look” of a fisheye image (Abraham and Förstner, 2005; Wei et al., 2012; Campos et al., 2018; Yin et al., 2018; Peng et al., 2020).…”