Open Access

In dieser Arbeit wird eine Methode zur Darstellung und Generierung von natürlich wirkendem Bewuchs auf besonders großen Arealen und unter Berücksichtigung ökologischer Faktoren vorgestellt. Die Generierung und Visualisierung von Bewuchs ist aufgrund der Komplexität biologischer Systeme und des Detailreichtums von Pflanzenmodellen ein herausforderndes Gebiet der Computergrafik und ermöglicht es, den Realismus von Landschaftsvisualisierungen erheblich zu steigern. Aufbauend auf [DMS06] wird bei Silva der Bewuchs so generiert, dass die zur Darstellung benötigten Wang-Kacheln und die mit ihnen assoziierten Teilverteilungen wiederverwendet werden können. Dazu wird ein Verfahren vorgestellt, um Poisson Disk Verteilungen mit variablen Radien auf nahtlosen Wang-Kachelmengen ohne rechenintensive globale Optimierung zu erzeugen. Durch die Einbeziehung von Nachbarschaften und frei konfigurierbaren Generierungspipelines können beliebige abiotische und biotische Faktoren bei der Bewuchsgenerierung berücksichtigt werden. Die durch Silva auf Wang-Kacheln erzeugten Pflanzenverteilungen ermöglichen, die darauf aufgebauten beschleunigenden Datenstrukturen bei der Visualisierung wieder zu verwenden. Durch Multi-Level Instancing und eine Schachtelung von Kd-Bäumen ist eine Visualisierung von großen bewachsenen Arealen mit geringen Renderzeiten und geringem Memoryfootprint von Hunderten Quadratkilometern Größe möglich.

In contrast to projection-based systems, large, high resolution multi-display systems offer a high pixel density on a large visualization area. This enables users to step up to the displays and see a small but highly detailed area. If the users move back a few steps they don't perceive details at pixel level but will instead get an overview of the whole visualization. Rendering techniques for design evaluation and review or for visualizing large volume data (e.g. Big Data applications) often use computationally expensive ray-based methods. Due to the number of pixels and the amount of data, these methods often do not achieve interactive frame rates. A view direction based (VDB) rendering technique renders the user's central field of view in high quality whereas the surrounding is rendered with a level-of-detail approach depending on the distance to the user's central field of view. This approach mimics the physiology of the human eye and conserves the advantage of highly detailed information when standing close to the multi-display system as well as the general overview of the whole scene. In this paper we propose a prototype implementation and evaluation of a focus-based rendering technique based on a hybrid ray tracing/sparse voxel octree rendering approach.

This presentation gives an overview of current research in the area of high quality rendering and visualization at the Institute of Visual Computing (IVC). Our research facility has some unique software and hardware installations of which we will describe a large, ultra- high resolution (72 megapixel) video wall in this presentation.

Human beings spend much time under the influence of artificial lighting. Often, it is beneficial to adapt lighting to the task, as well as the user’s mental and physical constitution and well-being. This formulates new requirements for lighting - human-centric lighting - and drives a need for new light control methods in interior spaces. In this paper we present a holistic system that provides a novel approach to human-centric lighting by introducing simulation methods into interactive light control, to adapt the lighting based on the user's needs. We look at a simulation and evaluation platform that uses interactive stochastic spectral rendering methods to simulate light sources, allowing for their interactive adjustment and adaption.

Head-mounted displays (HMDs) with integrated eye trackers have opened up a new realm for gaze-contingent rendering. The accurate estimation of gaze depth is essential when modeling the optical capabilities of the eye. Most recently multifocal displays are gaining importance, requiring focus estimates to control displays or lenses. Deriving the gaze depth solely by sampling the scene's depth at the point-of-regard fails for complex or thin objects as eye tracking is suffering from inaccuracies. Gaze depth measures using the eye's vergence only provide an accurate depth estimate for the first meter. In this work, we combine vergence measures and multiple depth measures into feature sets. This data is used to train a regression model to deliver improved estimates. We present a study showing that using multiple features allows for an accurate estimation of the focused depth (MSE<0.1m) over a wide range (first 6m).

In recent years, a variety of methods have been introduced to exploit the decrease in visual acuity of peripheral vision, known as foveated rendering. As more and more computationally involved shading is requested and display resolutions increase, maintaining low latencies is challenging when rendering in a virtual reality context. Here, foveated rendering is a promising approach for reducing the number of shaded samples. However, besides the reduction of the visual acuity, the eye is an optical system, filtering radiance through lenses. The lenses create depth-of-field (DoF) effects when accommodated to objects at varying distances. The central idea of this article is to exploit these effects as a filtering method to conceal rendering artifacts. To showcase the potential of such filters, we present a foveated rendering system, tightly integrated with a gaze-contingent DoF filter. Besides presenting benchmarks of the DoF and rendering pipeline, we carried out a perceptual study, showing that rendering quality is rated almost on par with full rendering when using DoF in our foveated mode, while shaded samples are reduced by more than 69%.

In order to achieve the highest possible performance, the ray traversal and intersection routines at the core of every high-performance ray tracer are usually hand-coded, heavily optimized, and implemented separately for each hardware platform—even though they share most of their algorithmic core. The results are implementations that heavily mix algorithmic aspects with hardware and implementation details, making the code non-portable and difficult to change and maintain. In this paper, we present a new approach that offers the ability to define in a functional language a set of conceptual, high-level language abstractions that are optimized away by a special compiler in order to maximize performance. Using this abstraction mechanism we separate a generic ray traversal and intersection algorithm from its low-level aspects that are specific to the target hardware. We demonstrate that our code is not only significantly more flexible, simpler to write, and more concise but also that the compiled results perform as well as state-of-the-art implementations on any of the tested CPU and GPU platforms.

We present a system which allows for guiding the image quality in global illumination (GI) methods by user-specified regions of interest (ROIs). This is done with either a tracked interaction device or a mouse-based method, making it possible to create a visualization with varying convergence rates throughout one image towards a GI solution. To achieve this, we introduce a scheduling approach based on Sparse Matrix Compression (SMC) for efficient generation and distribution of rendering tasks on the GPU that allows for altering the sampling density over the image plane. Moreover, we present a prototypical approach for filtering the newly, possibly sparse samples to a final image. Finally, we show how large-scale display systems can benefit from rendering with ROIs.

Computer graphics research strives to synthesize images of a high visual realism that are indistinguishable from real visual experiences. While modern image synthesis approaches enable to create digital images of astonishing complexity and beauty, processing resources remain a limiting factor. Here, rendering efficiency is a central challenge involving a trade-off between visual fidelity and interactivity. For that reason, there is still a fundamental difference between the perception of the physical world and computer-generated imagery. At the same time, advances in display technologies drive the development of novel display devices. The dynamic range, the pixel densities, and refresh rates are constantly increasing. Display systems enable a larger visual field to be addressed by covering a wider field-of-view, due to either their size or in the form of head-mounted devices. Currently, research prototypes are ranging from stereo and multi-view systems, head-mounted devices with adaptable lenses, up to retinal projection, and lightfield/holographic displays. Computer graphics has to keep step with, as driving these devices presents us with immense challenges, most of which are currently unsolved. Fortunately, the human visual system has certain limitations, which means that providing the highest possible visual quality is not always necessary. Visual input passes through the eye’s optics, is filtered, and is processed at higher level structures in the brain. Knowledge of these processes helps to design novel rendering approaches that allow the creation of images at a higher quality and within a reduced time-frame. This thesis presents the state-of-the-art research and models that exploit the limitations of perception in order to increase visual quality but also to reduce workload alike - a concept we call perception-driven rendering. This research results in several practical rendering approaches that allow some of the fundamental challenges of computer graphics to be tackled. By using different tracking hardware, display systems, and head-mounted devices, we show the potential of each of the presented systems. The capturing of specific processes of the human visual system can be improved by combining multiple measurements using machine learning techniques. Different sampling, filtering, and reconstruction techniques aid the visual quality of the synthesized images. An in-depth evaluation of the presented systems including benchmarks, comparative examination with image metrics as well as user studies and experiments demonstrated that the methods introduced are visually superior or on the same qualitative level as ground truth, whilst having a significantly reduced computational complexity.

Graph drawing with spring embedders employs a V x V computation phase over the graph's vertex set to compute repulsive forces. Here, the efficacy of forces diminishes with distance: a vertex can effectively only influence other vertices in a certain radius around its position. Therefore, the algorithm lends itself to an implementation using search data structures to reduce the runtime complexity. NVIDIA RT cores implement hierarchical tree traversal in hardware. We show how to map the problem of finding graph layouts with force-directed methods to a ray tracing problem that can subsequently be implemented with dedicated ray tracing hardware. With that, we observe speedups of 4x to 13x over a CUDA software implementation.

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.