Fortgeschrittene Entrauschungs-Verfahren und speicherlose Beschleunigungstechniken für realistische Bildsynthese

Stochastic ray tracing methods have become the industry's standard for today's realistic image synthesis thanks to their ability to achieve a supreme degree of realism by physically simulating various natural phenomena of light and cameras (e.g. global illumination, depth-of-field, or motion blur). Unfortunately, high computational cost for more complex scenes and image noise from insufficient simulations are major issues of these methods and, hence, acceleration and denoising are key components in stochastic ray tracing systems. In this thesis, we introduce two new filtering methods for advanced lighting and camera effects, as well as a novel approach for memoryless acceleration. In particular, we present an interactive filter for global illumination in the presence of depth-of-field, and a general and robust adaptive reconstruction framework for high-quality images with a wide range of rendering effects. To address complex scene geometry, we propose a novel concept which models the acceleration structure completely implicit, i.e. without any additional memory cost at all, while still allowing for interactive performance. Our contributions advance the state-of-the-art of denoising techniques for realistic image synthesis as well as the field of memoryless acceleration for ray tracing systems.Stochastische Ray-Tracing Methoden sind heutzutage der Industriestandard für realistische Bildsynthese, da sie einen hohen Grad an Realismus erzeugen können, indem sie verschiedene natürliche Phänomene (z.B. globale Beleuchtung, Tiefenunschärfe oder Bewegungsunschärfe) physikalisch korrekt simulieren. Offene Probleme dieser Verfahren sind hohe Rechenzeit für komplexere Szenen sowie Bildrauschen durch unzulängliche Simulationen. Demzufolge sind Beschleunigungstechniken und Entrauschungsverfahren essentielle Komponenten in stochastischen Ray-Tracing-Systemen. In dieser Arbeit stellen wir zwei neue Filter-Methoden für erweiterte Beleuchungs- und Kamera-Effekte sowie ein neuartiges Verfahren für eine speicherlose Beschleunigungsstruktur vor. Im Detail präsentieren wir einen interaktiven Filter für globale Beleuchtung in Kombination mit Tiefenunschärfe und einen generischen, robusten Ansatz für die adaptive Rekonstruktion von hoch-qualitativen Bildern mit einer großen Auswahl an Rendering-Effekten. Für das Problem hoher geometrischer Szenen-Komplexität demonstrieren wir ein neuartiges Konzept für die implizierte Modellierung der Beschleunigungsstruktur, welches keinen zusätzlichen Speicher verbraucht, aber weiterhin interaktive Laufzeiten ermöglicht. Unsere Beiträge verbessern sowohl den aktuellen Stand von Entrauschungs-Verfahren in der realistischen Bildsynthese als auch das Feld der speicherlosen Beschleunigungsstrukturen für Ray-Tracing-Systeme

Real-Time Noise Removal in Foveated Path Tracing

Path tracing is a method for rendering photorealistic two-dimensional images of three-dimensional scenes based on computing intersection between the scene geometry and light rays traveling through the scene. The rise in parallel computation resources in devices such as graphics processing units (GPUs) have made it more and more viable to do path tracing in real time. To achieve real-time performance, path tracing can be further optimized by using foveated rendering, where the properties of the human visual system are exploited to reduce the number of rays outside the central point of vision (fovea), where the human eye cannot discern fine detail. The reduction in the number of rays can, however, lead to several issues. Noise appears in the image as a result of an inadequate number of path tracing samples allocated to each pixel. Furthermore, the variation in the noise from one animation frame to the next appears as flicker. Finally, artifacts can appear when the spatially subsampled image is upsampled to a uniform resolution for display. In this thesis, solutions to the aforementioned issues are explored by implementing three noise removal methods into a foveated path tracing rendering system. The computational performance and the visual quality of the implemented methods is evaluated. Of the implemented methods, cross-bilateral filter provides the best quality, but its runtime doesn't scale well to large filter sizes. Large filter sizes are enabled by the À-Trous approximation of the cross-bilateral filter, at the cost of generating more artifacts in the result. Overall, while the implemented methods are able to provide visually pleasing results in some scenarios, improvements in the algorithms (e.g., local filter parameter selection) are needed to reach the quality seen in offline methods

Foveated Path Tracing with Fast Reconstruction and Efficient Sample Distribution

Polunseuranta on tietokonegrafiikan piirtotekniikka, jota on käytetty pääasiassa ei-reaaliaikaisen realistisen piirron tekemiseen. Polunseuranta tukee luonnostaan monia muilla tekniikoilla vaikeasti saavutettavia todellisen valon ilmiöitä kuten heijastuksia ja taittumista. Reaaliaikainen polunseuranta on hankalaa polunseurannan suuren laskentavaatimuksen takia. Siksi nykyiset reaaliaikaiset polunseurantasysteemi tuottavat erittäin kohinaisia kuvia, jotka tyypillisesti suodatetaan jälkikäsittelykohinanpoisto-suodattimilla. Erittäin immersiivisiä käyttäjäkokemuksia voitaisiin luoda polunseurannalla, joka täyttäisi laajennetun todellisuuden vaatimukset suuresta resoluutiosta riittävän matalassa vasteajassa. Yksi mahdollinen ratkaisu näiden vaatimusten täyttämiseen voisi olla katsekeskeinen polunseuranta, jossa piirron resoluutiota vähennetään katseen reunoilla. Tämän johdosta piirron laatu on katseen reunoilla sekä harvaa että kohinaista, mikä asettaa suuren roolin lopullisen kuvan koostavalle suodattimelle. Tässä työssä esitellään ensimmäinen reaaliajassa toimiva regressionsuodatin. Suodatin on suunniteltu kohinaisille kuville, joissa on yksi polunseurantanäyte pikseliä kohden. Nopea suoritus saavutetaan tiileissä käsittelemällä ja nopealla sovituksen toteutuksella. Lisäksi työssä esitellään Visual-Polar koordinaattiavaruus, joka jakaa polunseurantanäytteet siten, että niiden jakauma seuraa silmän herkkyysmallia. Visual-Polar-avaruuden etu muihin tekniikoiden nähden on että se vähentää työmäärää sekä polunseurannassa että suotimessa. Nämä tekniikat esittelevät toimivan prototyypin katsekeskeisestä polunseurannasta, ja saattavat toimia tienraivaajina laajamittaiselle realistisen reaaliaikaisen polunseurannan käyttöönotolle.Photo-realistic offline rendering is currently done with path tracing, because it naturally produces many real-life light effects such as reflections, refractions and caustics. These effects are hard to achieve with other rendering techniques. However, path tracing in real time is complicated due to its high computational demand. Therefore, current real-time path tracing systems can only generate very noisy estimate of the final frame, which is then denoised with a post-processing reconstruction filter. A path tracing-based rendering system capable of filling the high resolution in the low latency requirements of mixed reality devices would generate a very immersive user experience. One possible solution for fulfilling these requirements could be foveated path tracing, wherein the rendering resolution is reduced in the periphery of the human visual system. The key challenge is that the foveated path tracing in the periphery is both sparse and noisy, placing high demands on the reconstruction filter. This thesis proposes the first regression-based reconstruction filter for path tracing that runs in real time. The filter is designed for highly noisy one sample per pixel inputs. The fast execution is accomplished with blockwise processing and fast implementation of the regression. In addition, a novel Visual-Polar coordinate space which distributes the samples according to the contrast sensitivity model of the human visual system is proposed. The specialty of Visual-Polar space is that it reduces both path tracing and reconstruction work because both of them can be done with smaller resolution. These techniques enable a working prototype of a foveated path tracing system and may work as a stepping stone towards wider commercial adoption of photo-realistic real-time path tracing

Learning Sample-Based Monte Carlo Denoising from Noisy Training Data

Monte Carlo rendering allows for the production of high-quality photorealistic images of 3D scenes. However, producing noise-free images can take a considerable amount of compute resources. To lessen this burden and speed up the rendering process while maintaining similar quality, a lower-sample count image can be rendered and then denoised after rendering with image-space denoising methods. These methods are widely used in industry, and have recently enabled advancements in areas such as real-time ray tracing. While hand-tuned denoisers are available, the most successful denoising methods are based on machine learning with deep convolutional neural networks (CNNs). These denoisers are trained on large datasets of rendered images, consisting of pairs of low-sample count noisy images and the corresponding high-sample count reference images. Unfortunately, generating these datasets can be prohibitively expensive because of the cost of rendering thousands of high-sample count reference images. A potential solution to this problem comes from the Noise2Noise method, where denoisers can be learned solely from noisy training data. Lehtinen et al. applied their technique to Monte Carlo denoising, and were able to achieve similar performance to using clean reference images. However, their model was a proof of concept, and it is unclear whether the technique would work equally well with state-of-the-art Monte Carlo denoising methods. The authors also do not test their hypothesis that better results could be achieved by training on the additional noisy training data that could be generated with the same compute budget that was previously allocated to generating clean training data. Finally, it remains to be seen whether the authors' suggested parameters are equally effective when Noise2Noise is used with different denoising methods. In this thesis, I answer the above questions by applying Noise2Noise to a state-of-the-art Monte Carlo denoising algorithm called Sample-Based Monte-Carlo Denoising (SBMC). I adapt the SBMC scene generator to produce a dataset of noisy image pairs, use this dataset to train an SBMC-like CNN, and conduct experiments to determine the impact of various parameters on the performance of the denoiser. My results show that the Noise2Noise technique can be effectively applied to a state-of-the-art Monte Carlo denoising algorithm. I achieved comparable results to the original implementation at a significantly lower cost. I find that using additional training data can further improve these results, although more investigation is needed in this area. Finally, I detail the parameters that were necessary to achieve these results

Free-viewpoint Indoor Neural Relighting from Multi-view Stereo

We introduce a neural relighting algorithm for captured indoors scenes, that allows interactive free-viewpoint navigation. Our method allows illumination to be changed synthetically, while coherently rendering cast shadows and complex glossy materials. We start with multiple images of the scene and a 3D mesh obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is well-explained as the sum of a view-independent diffuse component and a view-dependent glossy term concentrated around the mirror reflection direction. We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation. We generate these input maps by exploiting the best elements of both image-based and physically-based rendering. We sample the input views to estimate diffuse scene irradiance, and compute the new illumination caused by user-specified light sources using path tracing. To facilitate the network's understanding of materials and synthesize plausible glossy reflections, we reproject the views and compute mirror images. We train the network on a synthetic dataset where each scene is also reconstructed with MVS. We show results of our algorithm relighting real indoor scenes and performing free-viewpoint navigation with complex and realistic glossy reflections, which so far remained out of reach for view-synthesis techniques

Visual Prototyping of Cloth

Realistic visualization of cloth has many applications in computer graphics. An ongoing research problem is how to best represent and capture appearance models of cloth, especially when considering computer aided design of cloth. Previous methods can be used to produce highly realistic images, however, possibilities for cloth-editing are either restricted or require the measurement of large material databases to capture all variations of cloth samples. We propose a pipeline for designing the appearance of cloth directly based on those elements that can be changed within the production process. These are optical properties of fibers, geometrical properties of yarns and compositional elements such as weave patterns. We introduce a geometric yarn model, integrating state-of-the-art textile research. We further present an approach to reverse engineer cloth and estimate parameters for a procedural cloth model from single images. This includes the automatic estimation of yarn paths, yarn widths, their variation and a weave pattern. We demonstrate that we are able to match the appearance of original cloth samples in an input photograph for several examples. Parameters of our model are fully editable, enabling intuitive appearance design. Unfortunately, such explicit fiber-based models can only be used to render small cloth samples, due to large storage requirements. Recently, bidirectional texture functions (BTFs) have become popular for efficient photo-realistic rendering of materials. We present a rendering approach combining the strength of a procedural model of micro-geometry with the efficiency of BTFs. We propose a method for the computation of synthetic BTFs using Monte Carlo path tracing of micro-geometry. We observe that BTFs usually consist of many similar apparent bidirectional reflectance distribution functions (ABRDFs). By exploiting structural self-similarity, we can reduce rendering times by one order of magnitude. This is done in a process we call non-local image reconstruction, which has been inspired by non-local means filtering. Our results indicate that synthesizing BTFs is highly practical and may currently only take a few minutes for small BTFs. We finally propose a novel and general approach to physically accurate rendering of large cloth samples. By using a statistical volumetric model, approximating the distribution of yarn fibers, a prohibitively costly, explicit geometric representation is avoided. As a result, accurate rendering of even large pieces of fabrics becomes practical without sacrificing much generality compared to fiber-based techniques