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Sandra Bischoff / Andreas Büsch / Gunter Geiger / Lothar Harles / Peter Holnick (Hg.): Was wird hier gespielt? Computerspiele in Familie 2020

Stefan Piasecki
2015 Communicatio Socialis  
Stefan Piasecki, Kassel Sandra Bischoff / Andreas Büsch / Gunter Geiger / Lothar Harles / Peter Holnick (Hg.): Was wird hier gespielt? Computerspiele in Fami- lie 2020.  ...  Andreas Büsch führt ein mit Grundbetrachtungen zu Formen des Spiels und dessen Wesen und deckt auch Devianzformen ab wie Spielesucht oder den Vorwurf, Computer-und Videospiele seien gewalterzeugend oder  ... 
doi:10.5771/0010-3497-2015-2-238 fatcat:gvsnb5ypwbdithukmmnmnku44i

Geiger

Stephen T. Jones, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau
2006 SIGPLAN notices  
Using case studies we show that the information provided by Geiger enables a VMM to implement useful VMM-level services.  ...  We have created a prototype implementation of these techniques inside the Xen VMM called Geiger and show that it can accurately infer when pages are inserted into and evicted from a system's buffer cache  ...  Geiger Techniques In this section we discuss the techniques used by Geiger.  ... 
doi:10.1145/1168918.1168861 fatcat:qailvkvejzhwhjiet53ozdlmtu

Geiger

Stephen T. Jones, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau
2006 SIGARCH Computer Architecture News  
Using case studies we show that the information provided by Geiger enables a VMM to implement useful VMM-level services.  ...  We have created a prototype implementation of these techniques inside the Xen VMM called Geiger and show that it can accurately infer when pages are inserted into and evicted from a system's buffer cache  ...  Geiger Techniques In this section we discuss the techniques used by Geiger.  ... 
doi:10.1145/1168919.1168861 fatcat:rzc4fd26bfaz5n5hmdsjyqdgaq

Counterfactual Generative Networks [article]

Axel Sauer, Andreas Geiger
2021 arXiv   pre-print
Andreas Geiger was supported by the ERC Starting Grant LEGO-3D (850533).  ... 
arXiv:2101.06046v1 fatcat:apykwyzkevee5hiii23lo2dpae

Geiger

Stephen T. Jones, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau
2006 ACM SIGOPS Operating Systems Review  
Using case studies we show that the information provided by Geiger enables a VMM to implement useful VMM-level services.  ...  We have created a prototype implementation of these techniques inside the Xen VMM called Geiger and show that it can accurately infer when pages are inserted into and evicted from a system's buffer cache  ...  Geiger Techniques In this section we discuss the techniques used by Geiger.  ... 
doi:10.1145/1168917.1168861 fatcat:x4emc7xewvb3xh7byx6eclokxe

Learning Neural Light Transport [article]

Paul Sanzenbacher, Lars Mescheder, Andreas Geiger
2020 arXiv   pre-print
In recent years, deep generative models have gained significance due to their ability to synthesize natural-looking images with applications ranging from virtual reality to data augmentation for training computer vision models. While existing models are able to faithfully learn the image distribution of the training set, they often lack controllability as they operate in 2D pixel space and do not model the physical image formation process. In this work, we investigate the importance of 3D
more » ... ing for photorealistic rendering. We present an approach for learning light transport in static and dynamic 3D scenes using a neural network with the goal of predicting photorealistic images. In contrast to existing approaches that operate in the 2D image domain, our approach reasons in both 3D and 2D space, thus enabling global illumination effects and manipulation of 3D scene geometry. Experimentally, we find that our model is able to produce photorealistic renderings of static and dynamic scenes. Moreover, it compares favorably to baselines which combine path tracing and image denoising at the same computational budget.
arXiv:2006.03427v1 fatcat:4knpevlvljdtjih4ne3liynlfy

Projected GANs Converge Faster [article]

Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger
2021 arXiv   pre-print
Andreas Geiger was supported by the ERC Starting Grant LEGO-3D (850533).  ... 
arXiv:2111.01007v1 fatcat:zelvwjh46bcftkywsuota7im4u

Abraham Geiger—skeptischer Pionier einer Glaubenslehre des Reformjudentums? [chapter]

Andreas Brämer, Bill Rebiger
2018 YEARBOOK OF THE MAIMONIDES CENTRE FOR ADVANCED STUDIES  
Geiger an LudwigGeiger,14. Januar 1866",inAbraham Geiger,hrsg. von Geiger, 179f. Abraham Geiger-skeptischer Pionier einer Glaubenslehredes Reformjudentums?  ...  .  "Geiger an Dernburg, 8.  ... 
doi:10.1515/9783110577686-011 fatcat:jopujttnkra4toq32nfqzaxlyy

Attacking Optical Flow [article]

Anurag Ranjan and Joel Janai and Andreas Geiger and Michael J. Black
2019 arXiv   pre-print
Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-driving cars, it is important to gain insights into the robustness of those techniques. Recently, it has been shown that adversarial attacks easily fool deep neural networks to misclassify objects. The robustness of optical flow networks to adversarial attacks, however, has not been studied so far. In this paper, we extend
more » ... dversarial patch attacks to optical flow networks and show that such attacks can compromise their performance. We show that corrupting a small patch of less than 1% of the image size can significantly affect optical flow estimates. Our attacks lead to noisy flow estimates that extend significantly beyond the region of the attack, in many cases even completely erasing the motion of objects in the scene. While networks using an encoder-decoder architecture are very sensitive to these attacks, we found that networks using a spatial pyramid architecture are less affected. We analyse the success and failure of attacking both architectures by visualizing their feature maps and comparing them to classical optical flow techniques which are robust to these attacks. We also demonstrate that such attacks are practical by placing a printed pattern into real scenes.
arXiv:1910.10053v1 fatcat:nnglqn525zfa3kz42f535jasga

Semantic Visual Localization [article]

Johannes L. Schönberger, Marc Pollefeys, Andreas Geiger, Torsten Sattler
2018 arXiv   pre-print
Schönberger 1 Marc Pollefeys 1,3 Andreas Geiger 1,2 Torsten Sattler 1 Semantic map 0°G lobal ambiguity Local ambiguity 90°180°F igure 1: Localization results for sequence 00 of the KITTI odometry dataset  ... 
arXiv:1712.05773v2 fatcat:imshheqdxbfrxa2yxdt7qron3u

Learning Implicit Surface Light Fields [article]

Michael Oechsle, Michael Niemeyer, Lars Mescheder, Thilo Strauss, Andreas Geiger
2020 arXiv   pre-print
Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis requires reasoning about the complex interplay of light, geometry and surface properties. In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field. In contrast to existing
more » ... tations, our implicit model represents surface light fields in a continuous fashion and independent of the geometry. Moreover, we condition the surface light field with respect to the location and color of a small light source. Compared to traditional surface light field models, this allows us to manipulate the light source and relight the object using environment maps. We further demonstrate the capabilities of our model to predict the visual appearance of an unseen object from a single real RGB image and corresponding 3D shape information. As evidenced by our experiments, our model is able to infer rich visual appearance including shadows and specular reflections. Finally, we show that the proposed representation can be embedded into a variational auto-encoder for generating novel appearances that conform to the specified illumination conditions.
arXiv:2003.12406v1 fatcat:kootewwvjbeldocprnk7p4uvau

SMD-Nets: Stereo Mixture Density Networks [article]

Fabio Tosi, Yiyi Liao, Carolin Schmitt, Andreas Geiger
2021 arXiv   pre-print
Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures which ameliorates both issues. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and
more » ... ecise disparity estimates near discontinuities while explicitly modeling the aleatoric uncertainty inherent in the observations. Moreover, we formulate disparity estimation as a continuous problem in the image domain, allowing our model to query disparities at arbitrary spatial precision. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets. Our experiments demonstrate increased depth accuracy near object boundaries and prediction of ultra high-resolution disparity maps on standard GPUs. We demonstrate the flexibility of our technique by improving the performance of a variety of stereo backbones.
arXiv:2104.03866v1 fatcat:h4ybsopdinaqzmpnrjnjpw2utq

Geometric Image Synthesis [article]

Hassan Abu Alhaija, Siva Karthik Mustikovela, Andreas Geiger, Carsten Rother
2018 arXiv   pre-print
The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image
more » ... ation method that leverages various types of scene information, including geometry and segmentation, to create realistic looking natural images that match the desired scene structure. Our geometrically-consistent image synthesis method is a deep neural network, called Geometry to Image Synthesis (GIS) framework, which retains the advantages of a trainable method, e.g., differentiability and adaptiveness, but, at the same time, makes a step towards the generalizability, control and quality output of modern graphics rendering engines. We utilize the GIS framework to insert vehicles in outdoor driving scenes, as well as to generate novel views of objects from the Linemod dataset. We qualitatively show that our network is able to generalize beyond the training set to novel scene geometries, object shapes and segmentations. Furthermore, we quantitatively show that the GIS framework can be used to synthesize large amounts of training data which proves beneficial for training instance segmentation models.
arXiv:1809.04696v2 fatcat:xbpdtuhb2fc63l5hptcff73mba

Supplemental Table S3

Axel Muendlein, Kathrin Geiger, Andreas Leiherer, Christoph H. Saely, Peter Fraunberger, Heinz Drexel
2019 Figshare  
Supplemental Table S3: Association between miRNAs and kidney function in the total study cohort
doi:10.6084/m9.figshare.10248308 fatcat:jvrdq4foendzfnfzd4cvr3ucb4

Supplemental Table S1

Axel Muendlein, Kathrin Geiger, Andreas Leiherer, Christoph H. Saely, Peter Fraunberger, Heinz Drexel
2019 Figshare  
Supplemental Table S1: MiRNAs selected for the present study
doi:10.6084/m9.figshare.10248260 fatcat:ug2f4s62hng7jdqrbhro47i3xa
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