Browsing by Author "Didyk, Piotr"

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    Gaze-Contingent Perceptual Level of Detail Prediction
    (The Eurographics Association, 2023) Surace, Luca; Tursun, Cara; Celikcan, Ufuk; Didyk, Piotr; Ritschel, Tobias; Weidlich, Andrea
    New virtual reality headsets and wide field-of-view displays rely on foveated rendering techniques that lower the rendering quality for peripheral vision to increase performance without a perceptible quality loss. While the concept is simple, the practical realization of the foveated rendering systems and their full exploitation are still challenging. Existing techniques focus on modulating the spatial resolution of rendering or shading rate according to the characteristics of human perception. However, most rendering systems also have a significant cost related to geometry processing. In this work, we investigate the problem of mesh simplification, also known as the level of detail (LOD) technique, for foveated rendering. We aim to maximize the amount of LOD simplification while keeping the visibility of changes to the object geometry under a selected threshold. We first propose two perceptually inspired visibility models for mesh simplification suitable for gaze-contingent rendering. The first model focuses on spatial distortions in the object silhouette and body. The second model accounts for the temporal visibility of switching between two LODs. We calibrate the two models using data from perceptual experiments and derive a computational method that predicts a suitable LOD for rendering an object at a specific eccentricity without objectionable quality loss. We apply the technique to the foveated rendering of static and dynamic objects and demonstrate the benefits in a validation experiment. Using our perceptually-driven gaze-contingent LOD selection, we achieve up to 33% of extra speedup in rendering performance of complex-geometry scenes when combined with the most recent industrial solutions, i.e., Nanite from Unreal Engine.
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    Neural Acceleration of Scattering-Aware Color 3D Printing
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Rittig, Tobias; Sumin, Denis; Babaei, Vahid; Didyk, Piotr; Voloboy, Alexey; Wilkie, Alexander; Bickel, Bernd; Myszkowski, Karol; Weyrich, Tim; Krivánek, Jaroslav; Mitra, Niloy and Viola, Ivan
    With the wider availability of full-color 3D printers, color-accurate 3D-print preparation has received increased attention. A key challenge lies in the inherent translucency of commonly used print materials that blurs out details of the color texture. Previous work tries to compensate for these scattering effects through strategic assignment of colored primary materials to printer voxels. To date, the highest-quality approach uses iterative optimization that relies on computationally expensive Monte Carlo light transport simulation to predict the surface appearance from subsurface scattering within a given print material distribution; that optimization, however, takes in the order of days on a single machine. In our work, we dramatically speed up the process by replacing the light transport simulation with a data-driven approach. Leveraging a deep neural network to predict the scattering within a highly heterogeneous medium, our method performs around two orders of magnitude faster than Monte Carlo rendering while yielding optimization results of similar quality level. The network is based on an established method from atmospheric cloud rendering, adapted to our domain and extended by a physically motivated weight sharing scheme that substantially reduces the network size. We analyze its performance in an end-to-end print preparation pipeline and compare quality and runtime to alternative approaches, and demonstrate its generalization to unseen geometry and material values. This for the first time enables full heterogenous material optimization for 3D-print preparation within time frames in the order of the actual printing time.
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    Selecting Texture Resolution Using a Task-specific Visibility Metric
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wolski, Krzysztof; Giunchi, Daniele; Kinuwaki, Shinichi; Didyk, Piotr; Myszkowski, Karol; Steed, Anthony; Mantiuk, Rafal K.; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    In real-time rendering, the appearance of scenes is greatly affected by the quality and resolution of the textures used for image synthesis. At the same time, the size of textures determines the performance and the memory requirements of rendering. As a result, finding the optimal texture resolution is critical, but also a non-trivial task since the visibility of texture imperfections depends on underlying geometry, illumination, interactions between several texture maps, and viewing positions. Ideally, we would like to automate the task with a visibility metric, which could predict the optimal texture resolution. To maximize the performance of such a metric, it should be trained on a given task. This, however, requires sufficient user data which is often difficult to obtain. To address this problem, we develop a procedure for training an image visibility metric for a specific task while reducing the effort required to collect new data. The procedure involves generating a large dataset using an existing visibility metric followed by refining that dataset with the help of an efficient perceptual experiment. Then, such a refined dataset is used to retune the metric. This way, we augment sparse perceptual data to a large number of per-pixel annotated visibility maps which serve as the training data for application-specific visibility metrics. While our approach is general and can be potentially applied for different image distortions, we demonstrate an application in a game-engine where we optimize the resolution of various textures, such as albedo and normal maps.