Neural Volume Super-Resolution

Neural volumetric representations have become a widely adopted model for radiance fields in 3D scenes. These representations are fully implicit or hybrid function approximators of the instantaneous volumetric radiance in a scene, which are typically learned from multi-view captures of the scene. We investigate the new task of neural volume super-resolution - rendering high-resolution views corresponding to a scene captured at low resolution. To this end, we propose a neural super-resolution network that operates directly on the volumetric representation of the scene. This approach allows us to exploit an advantage of operating in the volumetric domain, namely the ability to guarantee consistent super-resolution across different viewing directions. To realize our method, we devise a novel 3D representation that hinges on multiple 2D feature planes. This allows us to super-resolve the 3D scene representation by applying 2D convolutional networks on the 2D feature planes. We validate the proposed method's capability of super-resolving multi-view consistent views both quantitatively and qualitatively on a diverse set of unseen 3D scenes, demonstrating a significant advantage over existing approaches

A Generic Framework for Depth Reconstruction Enhancement

We propose a generic depth-refinement scheme based on GeoNet, a recent deep-learning approach for predicting depth and normals from a single color image, and extend it to be applied to any depth reconstruction task such as super resolution, denoising and deblurring, as long as the task includes a depth output. Our approach utilizes a tight coupling of the inherent geometric relationship between depth and normal maps to guide a neural network. In contrast to GeoNet, we do not utilize the original input information to the backbone reconstruction task, which leads to a generic application of our network structure. Our approach first learns a high-quality normal map from the depth image generated by the backbone method and then uses this normal map to refine the initial depth image jointly with the learned normal map. This is motivated by the fact that it is hard for neural networks to learn direct mapping between depth and normal maps without explicit geometric constraints. We show the efficiency of our method on the exemplary inverse depth-image reconstruction tasks of denoising, super resolution and removal of motion blur

Realistic lens distortion rendering

Rendering images with lens distortion that matches real cameras requires a camera model that allows calibration of relevant parameters based on real imagery. This requirement is not fulfilled for camera models typically used in the field of Computer Graphics. In this paper, we present two approaches to integrate realistic lens distortions effects into any graphics pipeline. Both approaches are based on the most widely used camera model in Computer Vision, and thus can reproduce the behavior of real calibrated cameras. The advantages and drawbacks of the two approaches are compared, and both are verified by recovering rendering parameters through a calibration performed on rendered images

Present and future challenges to quality science in research and practice

Die aktuelle Situation in der Industrie und im Dienstleistungssektor ist durch eine starke Veränderung der Geschäftsmodelle geprägt. Gekoppelt mit der Digitalisierung vieler Geschäftsprozesse bewirkt und erzwingt dies enorme Anpassungen in und zwischen den Organisationen. Diese drastischen Veränderungen müssen vom Qualitätsmanagement (QM) unmittelbar begleitet werden, um mit Methoden und Techniken des Qualitätsmanagements die Prozesse sicher umzugestalten und anschließend angepasste QM-Systeme in einem kontinuierlichen Verbesserungsprozess weiter zu optimieren. Hierzu gibt es keine einheitliche Vorgehensweise. Im Gegenteil, die Heterogenität der Branchen erfordert spezifische Anpassungs- und Umsetzungslösungen. Die GQW-Tagung 2017 trägt dazu bei, unterschiedliche Schwerpunkte in der Transformation der existierenden QM-Systeme im Hinblick auf die neuen Herausforderungen darzustellen.The current situation in industry and in the service sector is characterized by a strong change in business models. Linked with the digitalization of many business processes this leads to major adjustments in and between organizations. These radical changes have to be directly accompanied by quality management (QM) in order to redesign the processes safely with methods and techniques of quality management and subsequently to optimize adapted quality management systems in a continuous improvement process. There is no standardized approach to this. On the contrary, the heterogeneity of the sectors requires specific solutions for adaption and implementation. The GQW conference 2017 helps to show different key aspects in the transformation of existing quality management systems with regard to the new challenges