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Item Optimizing Disparity for Motion in Depth(The Eurographics Association and Blackwell Publishing Ltd., 2013) Kellnhofer, Petr; Ritschel, Tobias; Myszkowski, Karol; Seidel, Hans-Peter; Nicolas Holzschuch and Szymon RusinkiewiczBeyond the careful design of stereo acquisition equipment and rendering algorithms, disparity post-processing has recently received much attention, where one of the key tasks is to compress the originally large disparity range to avoid viewing discomfort. The perception of dynamic stereo content however, relies on reproducing the full disparity-time volume that a scene point undergoes in motion. This volume can be strongly distorted in manipulation, which is only concerned with changing disparity at one instant in time, even if the temporal coherence of that change is maintained. We propose an optimization to preserve stereo motion of content that was subject to an arbitrary disparity manipulation, based on a perceptual model of temporal disparity changes. Furthermore, we introduce a novel 3D warping technique to create stereo image pairs that conform to this optimized disparity map. The paper concludes with perceptual studies of motion reproduction quality and task performance in a simple game, showing how our optimization can achieve both viewing comfort and faithful stereo motion.Item Decomposing Single Images for Layered Photo Retouching(The Eurographics Association and John Wiley & Sons Ltd., 2017) Innamorati, Carlo; Ritschel, Tobias; Weyrich, Tim; Mitra, Niloy J.; Zwicker, Matthias and Sander, PedroPhotographers routinely compose multiple manipulated photos of the same scene into a single image, producing a fidelity difficult to achieve using any individual photo. Alternately, 3D artists set up rendering systems to produce layered images to isolate individual aspects of the light transport, which are composed into the final result in post-production. Regrettably, these approaches either take considerable time and effort to capture, or remain limited to synthetic scenes. In this paper, we suggest a method to decompose a single image into multiple layers that approximates effects such as shadow, diffuse illumination, albedo, and specular shading. To this end, we extend the idea of intrinsic images along two axes: first, by complementing shading and reflectance with specularity and occlusion, and second, by introducing directional dependence. We do so by training a convolutional neural network (CNN) with synthetic data. Such decompositions can then be manipulated in any off-the-shelf image manipulation software and composited back. We demonstrate the effectiveness of our decomposition on synthetic (i. e., rendered) and real data (i. e., photographs), and use them for photo manipulations, which are otherwise impossible to perform based on single images. We provide comparisons with state-of-the-art methods and also evaluate the quality of our decompositions via a user study measuring the effectiveness of the resultant photo retouching setup. Supplementary material and code are available for research use at geometry.cs.ucl.ac.uk/projects/2017/layered-retouching.Item Deep-learning the Latent Space of Light Transport(The Eurographics Association and John Wiley & Sons Ltd., 2019) Hermosilla, Pedro; Maisch, Sebastian; Ritschel, Tobias; Ropinski, Timo; Boubekeur, Tamy and Sen, PradeepWe suggest a method to directly deep-learn light transport, i. e., the mapping from a 3D geometry-illumination-material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi-transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two-stage operator comprising a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D-2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage-operator serves as a valuable extension to modern deferred shading approaches.Item Distortion-Free Displacement Mapping(The Eurographics Association and John Wiley & Sons Ltd., 2019) Zirr, Tobias; Ritschel, Tobias; Steinberger, Markus and Foley, TimDisplacement mapping is routinely used to add geometric details in a fast and easy-to-control way, both in offline rendering as well as recently in interactive applications such as games. However, it went largely unnoticed (with the exception of McGuire and Whitson [MW08]) that, when applying displacement mapping to a surface with a low-distortion parametrization, this parametrization is distorted as the geometry was changed by the displacement mapping. Typical resulting artifacts are ''rubber band''-like distortion patterns in areas of strong displacement change where a small isotropic area in texture space is mapped to a large anisotropic area in world space. We describe a fast, fully GPU-based two-step procedure to resolve this problem. First, a correction deformation is computed from the displacement map. Second, two variants to apply this correction when computing displacement mapping are proposed. The first variant is backward-compatible and can resolve the artifact in any rendering pipeline without modifying it and without requiring additional computation at render time, but only works for bijective parametrizations. The second variant works for more general parametrizations, but requires to modify the rendering code and incurs a very small computational overhead.Item Deep Shading: Convolutional Neural Networks for Screen Space Shading(The Eurographics Association and John Wiley & Sons Ltd., 2017) Nalbach, Oliver; Arabadzhiyska, Elena; Mehta, Dushyant; Seidel, Hans-Peter; Ritschel, Tobias; Zwicker, Matthias and Sander, PedroIn computer vision, convolutional neural networks (CNNs) achieve unprecedented performance for inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance. In computer graphics, screen space shading has boosted the quality of real-time rendering, converting the same kind of attributes of a virtual scene back to appearance, enabling effects like ambient occlusion, indirect light, scattering and many more. In this paper we consider the diagonal problem: synthesizing appearance from given per-pixel attributes using a CNN. The resulting Deep Shading renders screen space effects at competitive quality and speed while not being programmed by human experts but learned from example images.Item Pacific Graphics 2024 - CGF 43-7: Frontmatter(The Eurographics Association and John Wiley & Sons Ltd., 2024) Chen, Renjie; Ritschel, Tobias; Whiting, Emily; Chen, Renjie; Ritschel, Tobias; Whiting, EmilyItem Efficient Multi-image Correspondences for On-line Light Field Video Processing(The Eurographics Association and John Wiley & Sons Ltd., 2016) Dąbała, Łukasz; Ziegler, Matthias; Didyk, Piotr; Zilly, Frederik; Keinert, Joachim; Myszkowski, Karol; Seidel, Hans-Peter; Rokita, Przemysław; Ritschel, Tobias; Eitan Grinspun and Bernd Bickel and Yoshinori DobashiLight field videos express the entire visual information of an animated scene, but their shear size typically makes capture, processing and display an off-line process, i. e., time between initial capture and final display is far from real-time. In this paper we propose a solution for one of the key bottlenecks in such a processing pipeline, which is a reliable depth reconstruction possibly for many views. This is enabled by a novel correspondence algorithm converting the video streams from a sparse array of off-the-shelf cameras into an array of animated depth maps. The algorithm is based on a generalization of the classic multi-resolution Lucas-Kanade correspondence algorithm from a pair of images to an entire array. Special inter-image confidence consolidation allows recovery from unreliable matching in some locations and some views. It can be implemented efficiently in massively parallel hardware, allowing for interactive computations. The resulting depth quality as well as the computation performance compares favorably to other state-of-the art light field-to-depth approaches, as well as stereo matching techniques. Another outcome of this work is a data set of light field videos that are captured with multiple variants of sparse camera arrays.Item Manipulating Refractive and Reflective Binocular Disparity(The Eurographics Association and John Wiley and Sons Ltd., 2014) Dabala, Lukasz; Kellnhofer, Petr; Ritschel, Tobias; Didyk, Piotr; Templin, Krzysztof; Myszkowski, Karol; Rokita, P.; Seidel, Hans-Peter; B. Levy and J. KautzPresenting stereoscopic content on 3D displays is a challenging task, usually requiring manual adjustments. A number of techniques have been developed to aid this process, but they account for binocular disparity of surfaces that are diffuse and opaque only. However, combinations of transparent as well as specular materials are common in the real and virtual worlds, and pose a significant problem. For example, excessive disparities can be created which cannot be fused by the observer. Also, multiple stereo interpretations become possible, e. g., for glass, that both reflects and refracts, which may confuse the observer and result in poor 3D experience. In this work, we propose an efficient method for analyzing and controlling disparities in computer-generated images of such scenes where surface positions and a layer decomposition are available. Instead of assuming a single per-pixel disparity value, we estimate all possibly perceived disparities at each image location. Based on this representation, we define an optimization to find the best per-pixel camera parameters, assuring that all disparities can be easily fused by a human. A preliminary perceptual study indicates, that our approach combines comfortable viewing with realistic depiction of typical specular scenes.Item Spectral Ray Differentials(The Eurographics Association and John Wiley and Sons Ltd., 2014) Elek, Oskar; Bauszat, Pablo; Ritschel, Tobias; Magnor, Marcus; Seidel, Hans-Peter; Wojciech Jarosz and Pieter PeersLight refracted by a dispersive interface leads to beautifully colored patterns that can be rendered faithfully with spectral Monte-Carlo methods. Regrettably, results often suffer from chromatic noise or banding, requiring high sampling rates and large amounts of memory compared to renderers operating in some trichromatic color space. Addressing this issue, we introduce spectral ray differentials, which describe the change of light direction with respect to changes in the spectrum. In analogy with the classic ray and photon differentials, this information can be used for filtering in the spectral domain. Effectiveness of our approach is demonstrated by filtering for offline spectral light and path tracing as well as for an interactive GPU photon mapper based on splatting. Our results show considerably less chromatic noise and spatial aliasing while retaining good visual similarity to reference solutions with negligible overhead in the order of milliseconds.Item Blue Noise Plots(The Eurographics Association and John Wiley & Sons Ltd., 2021) Onzenoodt, Christian van; Singh, Gurprit; Ropinski, Timo; Ritschel, Tobias; Mitra, Niloy and Viola, IvanWe propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often onedimensional strip plots are used to depict such data, one of their main problems is visual clutter which results from overlap. To reduce this overlap, jitter plots were introduced, whereby an additional, non-encoding plot dimension is introduced, along which the data point representing dots are randomly perturbed. Unfortunately, this randomness can suggest non-existent clusters, and often leads to visually unappealing plots, in which overlap might still occur. To overcome these shortcomings, we introduce Blue Noise Plots where random jitter along the non-encoding plot dimension is replaced by optimizing all dots to keep a minimum distance in 2D i. e., Blue Noise. We evaluate the effectiveness as well as the aesthetics of Blue Noise Plots through both, a quantitative and a qualitative user study. The Python implementation of Blue Noise Plots is available here.