The developments in optical metrology and computer vision require more and more advanced camera models. Their geometric calibration is of essential importance. Usually, low-dimensional models are used, which however often have insufficient accuracy for the respective applications. A more sophisticated approach uses the generalized camera model. Here, each pixel is described individually by its geometric ray properties. Our efforts in this article strive to improve this model. Hence, we propose a new approach for calibration. Moreover, we show how the immense number of parameters can be efficiently calculated and how the measurement uncertainties of reference features can be effectively utilized. We demonstrate the benefits of our method through an extensive evaluation of different cameras, namely a standard webcam and a microlens-based light field camera.

In the emerging field of computational imaging, rapid prototyping of new camera concepts becomes increasingly difficult since the signal processing is intertwined with the physical design of a camera. As novel computational cameras capture information other than the traditional two-dimensional information, ground truth data, which can be used to thoroughly benchmark a new system design, is also hard to acquire. We propose to bridge this gap by using simulation. In this article, we present a raytracing framework tailored for the design and evaluation of computational imaging systems. We show that, depending on the application, the image formation on a sensor and phenomena like image noise have to be simulated accurately in order to achieve meaningful results while other aspects, such as photorealistic scene modeling, can be omitted. Therefore, we focus on accurately simulating the mandatory components of computational cameras, namely apertures, lenses, spectral filters and sensors. Besides the simulation of the imaging process, the framework is capable of generating various ground truth data, which can be used to evaluate and optimize the performance of a particular imaging system. Due to its modularity, it is easy to further extend the framework to the needs of other fields of application. We make the source code of our simulation framework publicly available and encourage other researchers to use it to design and evaluate their own camera designs. 1

Zusammenfassung Die optische dreidimensionale Formerfassung spiegelnd reflektierender Objekte ist in der Messtechnik immer noch eine schwierige Aufgabe. Die Deflektometrie rekonstruiert die Oberfläche durch Beobachtung verzerrter Bilder einer reflektierten Referenzszene. In diesem Beitrag wird erstmalig ein neuer Ansatz präsentiert, bei dem das deflektometrische Messverfahren effizient mit einer Lichtfeldkamera kombiniert wird. Eine Interpretation der Kamera als hochgradig multiples Kamera-Array erlaubt es, das deflektometrische Messproblem mit einem Multi-Stereo-Ansatz zu lösen. Die Leistungsfähigkeit der Oberflächenrekonstruktion wird in experimentellen Messungen bestätigt und es zeigt sich, dass durch die Kombination von Deflektometrie mit Lichtfeldkameras hohe Genauigkeiten erreicht werden können.

ZusammenfassungDie Deflektometrie analysiert Messproben durch indirekte Beobachtung einer bekannten Referenzszene als Reflexion in der Oberfläche. Sie benötigt jedoch Zusatzwissen, z.B. über mindestens einen Oberflächenpunkt, um sehr präzise Oberflächengradienten zu messen. Lichtfeldkameras als optische 3D-Messgeräte erlauben es bei teilspiegelnden Oberflächen, die Entfernung sowohl zur Oberfläche als auch zur Referenzszene zu messen. In diesem Beitrag wird ein Ansatz präsentiert, bei dem diese doppelte Tiefenschätzung effektiv mit der Deflektometrie kombiniert wird. Durch Formulieren der Oberflächenrekonstruktion als Variationsproblem wird die Tiefenschätzung mit den deflektometrischen Gradienten fusioniert. Experimente bestätigen die Methode und zeigen, dass hohe Rekonstruktionsgenauigkeiten erreicht werden.

Sophisticated and highly specialized optical measuring devices are becoming increasingly important for high-precision manufacturing and environment perception. In particular, light field cameras are experiencing an ever-increasing interest in research and industry as they enable a variety of new measurement methods. Unfortunately, due to their complex structure, their calibration is very difficult and usually precisely tailored to the particular type of light field camera. To overcome these difficulties, we present a method that decodes a light field from the raw data of any light field imaging system without knowing and modeling the internal optical elements. We calibrate the camera using a precise generic calibration method and transform the obtained ray set into an equivalent light field representation. Finally, we reconstruct a rectified light field from the irregularly sampled data and in addition we derive the geometric ray properties as intrinsic camera parameters. Experimental results validate the method by showing that both the information of the observed scene and the geometric structure of the light field are preserved by an adequate rectification and calibration.