Browsing by Author "Seidel, Hans-Peter"

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    Learning a Self-supervised Tone Mapping Operator via Feature Contrast Masking Loss
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Chao; Chen, Bin; Seidel, Hans-Peter; Myszkowski, Karol; Serrano, Ana; Chaine, Raphaëlle; Kim, Min H.
    High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning-based methods is limited by the training data. Most existing works use as training set a curated selection of best-performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning-based self-supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics.
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    Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Bemana, Mojtaba; Keinert, Joachim; Myszkowski, Karol; Bätz, Michel; Ziegler, Matthias; Seidel, Hans-Peter; Ritschel, Tobias; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Image metrics predict the perceived per-pixel difference between a reference image and its degraded (e. g., re-rendered) version. In several important applications, the reference image is not available and image metrics cannot be applied. We devise a neural network architecture and training procedure that allows predicting the MSE, SSIM or VGG16 image difference from the distorted image alone while the reference is not observed. This is enabled by two insights: The first is to inject sufficiently many un-distorted natural image patches, which can be found in arbitrary amounts and are known to have no perceivable difference to themselves. This avoids false positives. The second is to balance the learning, where it is carefully made sure that all image errors are equally likely, avoiding false negatives. Surprisingly, we observe that the resulting no-reference metric, subjectively, can even perform better than the reference-based one, as it had to become robust against mis-alignments. We evaluate the effectiveness of our approach in an image-based rendering context, both quantitatively and qualitatively. Finally, we demonstrate two applications which reduce light field capture time and provide guidance for interactive depth adjustment.
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    Tessellated Shading Streaming
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Hladky, Jozef; Seidel, Hans-Peter; Steinberger, Markus; Boubekeur, Tamy and Sen, Pradeep
    Presenting high-fidelity 3D content on compact portable devices with low computational power is challenging. Smartphones, tablets and head-mounted displays (HMDs) suffer from thermal and battery-life constraints and thus cannot match the render quality of desktop PCs and laptops. Streaming rendering enables to show high-quality content but can suffer from potentially high latency. We propose an approach to efficiently capture shading samples in object space and packing them into a texture. Streaming this texture to the client, we support temporal frame up-sampling with high fidelity, low latency and high mobility. We introduce two novel sample distribution strategies and a novel triangle representation in the shading atlas space. Since such a system requires dynamic parallelism, we propose an implementation exploiting the power of hardware-accelerated tessellation stages. Our approach allows fast de-coding and rendering of extrapolated views on a client device by using hardwareaccelerated interpolation between shading samples and a set of potentially visible geometry. A comparison to existing shading methods shows that our sample distributions allow better client shading quality than previous atlas streaming approaches and outperforms image-based methods in all relevant aspects.