DSpace Repository :: Browsing by Author "Dachsbacher, Carsten"

Browsing by Author "Dachsbacher, Carsten"

Now showing 1 - 11 of 11

Frontmatter: VMV 2018: Vision, Modeling, and Visualization
(The Eurographics Association, 2018) Beck, Fabian; Dachsbacher, Carsten; Sadlo, Filip; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
Hybrid Online Autotuning for Parallel Ray Tracing
(The Eurographics Association, 2019) Herveau, Killian; Pfaffe, Philip; Tillmann, Martin Peter; Tichy, Walter F.; Dachsbacher, Carsten; Childs, Hank and Frey, Steffen
Acceleration structures are key to high performance parallel ray tracing. Maximizing performance requires configuring the degrees of freedom (e.g., construction parameters) these data structures expose. Whether a parameter setting is optimal depends on the input (e.g., the scene and view parameters) and hardware. Manual selection is tedious, error prone, and is not portable. To automate the parameter selection task we use a hybrid of model-based prediction and online autotuning. The combination benefits from the best of both worlds: one-shot configuration selection when inputs are known or similar, effective exploration of the configuration space otherwise. Online tuning additionally serves to train the model on real inputs without requiring a-priori training samples. Online autotuning outperforms best-practice configurations recommended by the literature, by up to 11% median. The model predictions achieve 95% of the online autotuning performance while reducing 90% of the autotuner overhead. Hybrid online autotuning thus enables always-on tuning of parallel ray tracing.
Importance Sampling of Glittering BSDFs based on Finite Mixture Distributions
(The Eurographics Association, 2021) Chermain, Xavier; Sauvage, Basile; Dischler, Jean-Michel; Dachsbacher, Carsten; Bousseau, Adrien and McGuire, Morgan
We propose an importance sampling scheme for the procedural glittering BSDF of Chermain et al. [CSDD20]. Glittering BSDFs have multi-lobe visible normal distribution functions (VNDFs) which are difficult to sample. They are typically sampled using a mono-lobe Gaussian approximation, leading to high variance and fireflies in the rendering. Our method optimally samples the multi-lobe VNDF, leading to lower variance and removing firefly artefacts at equal render time. It allows, for example, the rendering of glittering glass which requires an efficient sampling of the BSDF. The procedural VNDF of Chermain et al. is a finite mixture of tensor products of two 1D tabulated distributions. We sample the visible normals from their VNDF by first drawing discrete variables according to the mixture weights and then sampling the corresponding 1D distributions using the technique of inverse cumulative distribution functions (CDFs). We achieve these goals by tabulating and storing the CDFs, which uses twice the memory as the original work. We prove the optimality of our VNDF sampling and validate our implementation with statistical tests.
Minimal Convolutional Neural Networks for Temporal Anti Aliasing
(The Eurographics Association, 2023) Herveau, Killian; Piochowiak, Max; Dachsbacher, Carsten; Bikker, Jacco; Gribble, Christiaan
Existing deep learning methods for performing temporal anti aliasing (TAA) in rendering are either closed source or rely on upsampling networks with a large operation count which are expensive to evaluate. We propose a simple deep learning architecture for TAA combining only a few common primitives, easy to assemble and to change for application needs. We use a fully-convolutional neural network architecture with recurrent temporal feedback, motion vectors and depth values as input and show that a simple network can produce satisfactory results. Our architecture template, for which we provide code, introduces a method that adapts to different temporal subpixel offsets for accumulation without increasing the operation count. To this end, convolutional layers cycle through a set of different weights per temporal subpixel offset while their operations remain fixed. We analyze the effect of this method on image quality and present different tradeoffs for adapting the architecture. We show that our simple network performs remarkably better than variance clipping TAA, eliminating both flickering and ghosting without performing upsampling.
Moment-Based Opacity Optimization
(The Eurographics Association, 2020) Zeidan, Mahmoud; Rapp, Tobias; Peters, Christoph; Dachsbacher, Carsten; Frey, Steffen and Huang, Jian and Sadlo, Filip
Geometric structures such as points, lines, and surfaces play a vital role in scientific visualization. However, these visualizations frequently suffer from visual clutter that hinders the inspection of important features behind dense but less important features. In the past few years, geometric cluttering and occlusion avoidance has been addressed in scientific visualization with various approaches such as opacity optimization techniques. In this paper, we present a novel approach for opacity optimization based on recent state-of-the-art moment-based techniques for signal reconstruction. In contrast to truncated Fourier series, momentbased reconstructions of feature importance and optical depth along view rays are highly accurate for sparse regions but also plausible for densely covered regions. At the same time, moment-based methods do not suffer from ringing artifacts. Moreover, this representation enables fast evaluation and compact storage, which is crucial for per-pixel optimization especially for large geometric structures. We also present a fast screen space filtering approach for optimized opacities that works directly on moment buffers. This filtering approach is suitable for real-time visualization applications, while providing comparable quality to object space smoothing. Its implementation is independent of the type of geometry such that it is general and easy to integrate. We compare our technique to recent state of the art techniques for opacity optimization and apply it to real and synthetic data sets in various applications.
Path Guiding with Vertex Triplet Distributions
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Schüßler, Vincent; Hanika, Johannes; Jung, Alisa; Dachsbacher, Carsten; Ghosh, Abhijeet; Wei, Li-Yi
Good importance sampling strategies are decisive for the quality and robustness of photorealistic image synthesis with Monte Carlo integration. Path guiding approaches use transport paths sampled by an existing base sampler to build and refine a guiding distribution. This distribution then guides subsequent paths in regions that are otherwise hard to sample. We observe that all terms in the measurement contribution function sampled during path construction depend on at most three consecutive path vertices. We thus propose to build a 9D guiding distribution over vertex triplets that adapts to the full measurement contribution with a 9D Gaussian mixture model (GMM). For incremental path sampling, we query the model for the last two vertices of a path prefix, resulting in a 3D conditional distribution with which we sample the next vertex along the path. To make this approach scalable, we partition the scene with an octree and learn a local GMM for each leaf separately. In a learning phase, we sample paths using the current guiding distribution and collect triplets of path vertices. We resample these triplets online and keep only a fixed-size subset in reservoirs. After each progression, we obtain new GMMs from triplet samples by an initial hard clustering followed by expectation maximization. Since we model 3D vertex positions, our guiding distribution naturally extends to participating media. In addition, the symmetry in the GMM allows us to query it for paths constructed by a light tracer. Therefore our method can guide both a path tracer and light tracer from a jointly learned guiding distribution.
Perceptually Guided Automatic Parameter Optimization for Interactive Visualization
(The Eurographics Association, 2023) Opitz, Daniel; Zirr, Tobias; Dachsbacher, Carsten; Tessari, Lorenzo; Guthe, Michael; Grosch, Thorsten
We propose a new reference-free method for automatically optimizing the parameters of visualization techniques such that the perception of visual structures is improved. Manual tuning may require domain knowledge not only in the field of the analyzed data, but also deep knowledge of the visualization techniques, and thus often becomes challenging as the number of parameters that impact the result grows. To avoid this laborious and difficult task, we first derive an image metric that models the loss of perceived information in the processing of a displayed image by a human observer; good visualization parameters minimize this metric. Our model is loosely based on quantitative studies in the fields of perception and biology covering visual masking, photo receptor sensitivity, and local adaptation. We then pair our metric with a generic parameter tuning algorithm to arrive at an automatic optimization method that is oblivious to the concrete relationship between parameter sets and visualization. We demonstrate our method for several volume visualization techniques, where visual clutter, visibility of features, and illumination are often hard to balance. Since the metric can be efficiently computed using image transformations, it can be applied to many visualization techniques and problem settings in a unified manner, including continuous optimization during interactive visual exploration. We also evaluate the effectiveness of our approach in a user study that validates the improved perception of visual features in results optimized using our model of perception.
Portal-Based Path Perturbation for Metropolis Light Transport
(The Eurographics Association, 2020) Otsu, Hisanari; Hanika, Johannes; Dachsbacher, Carsten; Krüger, Jens and Niessner, Matthias and Stückler, Jörg
Light transport simulation in scenes with difficult visibility still remains a challenging problem. Markov chain Monte Carlo (MCMC) rendering is often employed for such configurations. It generates a sequence of correlated light transport paths by iteratively mutating the current state, a path, to another. Since the proposed path is correlated to the current path, MCMC can explore regions of the path space, also with difficult visibility, once they have been found. To improve the efficiency of the exploration, we propose a path mutation strategy making use of the concept of portals. Portals are user-defined objects in the scene to guide the sampling of the difficult visibility, which have been employed in the context of non-MCMC rendering. Our mutation strategy perturbs a path edge around the intersection point of the edge and the portal, instead of perturbing the edge by moving a path vertex as in the ordinary path mutation strategies. This reduces the probability for the proposed path being rejected due to changes in visibility.
Re‐Weighting Firefly Samples for Improved Finite‐Sample Monte Carlo Estimates
(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Zirr, Tobias; Hanika, Johannes; Dachsbacher, Carsten; Chen, Min and Benes, Bedrich
Samples with high contribution but low probability density, often called fireflies, occur in all practical Monte Carlo estimators and are part of computing unbiased estimates. For finite‐sample estimates, however, they can lead to excessive variance. Rejecting all samples classified as outliers, as suggested in previous work, leads to estimates that are too low and can cause undesirable artefacts. In this paper, we show how samples can be re‐weighted depending on their contribution and sampling frequency such that the finite‐sample estimate gets closer to the correct expected value and the variance can be controlled. For this, we first derive a theory for how samples should ideally be re‐weighted and that this would require the probability density function of the optimal sampling strategy. As this probability density function is generally unknown, we show how the discrepancy between the optimal and the actual sampling strategy can be estimated and used for re‐weighting in practice. We describe an efficient algorithm that allows for the necessary analysis of per‐pixel sample distributions in the context of Monte Carlo rendering without storing any individual samples, with only minimal changes to the rendering algorithm. It causes negligible runtime overhead, works in constant memory and is well suited for parallel and progressive rendering. The re‐weighting runs as a fast post‐process, can be controlled interactively and our approach is non‐destructive in that the unbiased result can be reconstructed at any time.Samples with high contribution but low probability density, often called fireflies, occur in all practical Monte Carlo estimators and are part of computing unbiased estimates. For finite‐sample estimates, however, they can lead to excessive variance. Rejecting all samples classified as outliers, as suggested in previous work, leads to estimates that are too low and can cause undesirable artefacts. In this paper, we show how samples can be re‐weighted depending on their contribution and sampling frequency such that the finite‐sample estimate gets closer to the correct expected value and the variance can be controlled. For this, we first derive a theory for how samples should ideally be re‐weighted and that this would require the probability density function of the optimal sampling strategy. As this probability density function is generally unknown, we show how the discrepancy between the optimal and the actual sampling strategy can be estimated and used for re‐weighting in practice. We describe an efficient algorithm that allows for the necessary analysis of per‐pixel sample distributions in the context of Monte Carlo rendering without storing any individual samples, with only minimal changes to the rendering algorithm.
Spectral Rendering with the Bounded MESE and sRGB Data
(The Eurographics Association, 2019) Peters, Christoph; Merzbach, Sebastian; Hanika, Johannes; Dachsbacher, Carsten; Klein, Reinhard and Rushmeier, Holly
In a recent journal paper, we introduced a technique to represent reflectance spectra by an arbitrary number of Fourier coefficients. As a special case, we converted tristimulus data to three Fourier coefficients. After summarizing this work, we introduce the Fourier sRGB color space. It is defined in terms of Fourier coefficients but designed to behave similar to sRGB. Textures stored in Fourier sRGB support efficient spectral rendering but can be compressed with techniques designed for sRGB textures. Compression errors are similar to sRGB.
Wide Gamut Spectral Upsampling with Fluorescence
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Jung, Alisa; Wilkie, Alexander; Hanika, Johannes; Jakob, Wenzel; Dachsbacher, Carsten; Boubekeur, Tamy and Sen, Pradeep
Physically based spectral rendering has become increasingly important in recent years. However, asset textures in such systems are usually still drawn or acquired as RGB tristimulus values. While a number of RGB to spectrum upsampling techniques are available, none of them support upsampling of all colours in the full spectral locus, as it is intrinsically bigger than the gamut of physically valid reflectance spectra. But with display technology moving to increasingly wider gamuts, the ability to achieve highly saturated colours becomes an increasingly important feature. Real materials usually exhibit smooth reflectance spectra, while computationally generated spectra become more blocky as they represent increasingly bright and saturated colours. In print media, plastic or textile design, fluorescent dyes are added to extend the boundaries of the gamut of reflectance spectra. We follow the same approach for rendering: we provide a method which, given an input RGB tristimulus value, automatically provides a mixture of a regular, smooth reflectance spectrum plus a fluorescent part. For highly saturated input colours, the combination yields an improved reconstruction compared to what would be possible relying on a reflectance spectrum alone. At the core of our technique is a simple parametric spectral model for reflectance, excitation, and emission that allows for compact storage and is compatible with texture mapping. The model can then be used as a fluorescent diffuse component in an existing more complex BRDF model. We also provide importance sampling routines for practical application in a path tracer.

Browsing by Author "Dachsbacher, Carsten"

Results Per Page

Sort Options