Deep Video Color Propagation

Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both spatially and temporally. These methods, however, are computationally expensive and do not take advantage of semantic information of the scene. In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range. Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.Comment: BMVC 201

Variational surface reconstruction

The demand for capturing 3D models of real world objects or scenes has steadily increased in the past. Today, there are numerous developments that indicate an even greater importance in the future: Computer generated special effects are extensively used and highly benefit from such data, 3D printing is starting to become more affordable, and the ability to conveniently include 3D content in websites has quite matured. Thus, 3D reconstruction has been and still is one of the most important research topics in the area of computer vision. Here, the reconstruction of a 3D model from a number of colour images with given camera poses is one of the most common tasks known as multi-view stereo. We contribute to the two main stages that arise in popular strategies for solving this problem: The estimation of depth maps from multiple views and the integration of multiple depth maps into a single watertight surface. Subsequently, we relax the constraint that the camera poses have to be known and present a novel pipeline for 3D reconstruction from image sequences that solely relies on dense ideas. It proves to be an interesting alternative to popular sparse approaches and leads to competitive results. When relying on sparse features, this only allows to estimate an oriented point cloud instead of a surface. To this end, we finally propose a general higher order framework for the surface reconstruction from oriented points.In den letzten Jahrzehnten ist die Nachfrage nach digitalen 3D Modellen von Objekten und Szenen ständig gestiegen und vieles spricht dafür, dass sich dies auch in Zukunft fortsetzt: Computergenerierte Spezialeffekte werden immer flächendeckender eingesetzt, der Druck von dreidimensionalen Gegenständen macht große Fortschritte, und die Darstellung dreidimensionaler Modelle im Webbrowser wird immer ausgereifter. Deshalb ist die 3D Rekonstruktion eines der wichtigsten Forschungsthemen im Bereich des maschinellen Sehens. Die Rekonstruktion von einem 3D Modell aus mehreren Bildern mit gegebenen Kameramatritzen ist hier eine der häufigsten Problemstellungen, bekannt als multi-view stereo. Wir leisten einen Beitrag zu den zwei wichtigen Schritten, die in multi-view stereo Ansätzen angewandt werden: Die Schätzung von Tiefenkarten aus mehreren Bildern und die Fusion von mehreren Tiefenkarten zu einem einzigen 3D Modell. Anschließend lockern wir die Voraussetzung, dass die Kameramatritzen bekannt sein müssen und präsentieren ein neues Verfahren zur 3D Rekonstruktion aus Bildsequenzen, das vollständig auf dichten Ansätzen beruht. Dies erweist sich als interessante Alternative zu populären Methoden, die mit einzelnen Merkmalen arbeiten. Verfahren, die auf einzelnen Merkmalen beruhen, erlauben die Schätzung von orientierten Punktwolken. Daher entwickeln wir zum Schluss ein allgemeines Rahmenwerk für die Berechnung von wasserdichten Oberflächen aus orientierten Punktwolken

A Fully Progressive Approach to Single-Image Super-Resolution

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [34]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster