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Lens flare is a visual phenomenon caused by interreflection of light within a lens system. This effect is often seen as an undesired artifact, but it also gives rendered images a realistic appearance and is even used for artistic purposes. In the area of computer graphics, several simulation-based approaches have been presented to render lens flare for a given spherical lens system. For physically reliable results, these approaches require an accurate description of that system, which differs from camera to camera. Also, for the lens flares appearance, crucial parameters—especially the anti-reflection coatings—can often only be approximated. In this paper we present a novel workflow for generating physically plausible renderings of lens flare phenomena by analyzing the lens flares captured with a camera. Our method allows predicting the occurrence of lens flares for a given light setup. This is an often requested feature in light planning applications in order to efficiently avoid lens flare-prone light positioning. A model with a tight parameter set and a GPU-based rendering method allows our approach to be used in real-time applications.

In computer graphics, stochastic sampling is frequently used to efficiently approximate complex functions and integrals. The error of approximation can be reduced by distributing samples according to an importance function, but cannot be eliminated completely. To avoid visible artifacts, sample distributions are sought to be random, but spatially uniform, which is called blue-noise sampling. The generation of unbiased, importance-guided blue-noise samples is expensive and not feasible for real-time applications. Sampling algorithms for these applications focus on runtime performance at the cost of having weak blue-noise properties. Blue-noise distributions have also been proposed for digital halftoning in the form of precomputed dither matrices. Ordered dithering with such matrices allows to distribute dots with blue-noise properties according to a grayscale image. By the nature of ordered dithering, this process can be parallelized easily. We introduce a novel sampling method called forced random sampling that is based on forced random dithering, a variant of ordered dithering with blue noise. By shifting the main computational effort into the generation of a precomputed dither matrix, our sampling method runs efficiently on GPUs and allows real-time importance sampling with blue noise for a finite number of samples. We demonstrate the quality of our method in two different rendering applications.

State-of-the-art lighting design is based on physically accurate lighting simulations of scenes such as offices. The simulation results support lighting designers in the creation of lighting configurations, which must meet contradicting customer objectives regarding quality and price while conforming to industry standards. However, current tools for lighting design impede rapid feedback cycles. On the one side, they decouple analysis and simulation specification. On the other side, they lack capabilities for a detailed comparison of multiple configurations. The primary contribution of this paper is a design study of LiteVis, a system for efficient decision support in lighting design. LiteVis tightly integrates global illumination-based lighting simulation, a spatial representation of the scene, and non-spatial visualizations of parameters and result indicators. This enables an efficient iterative cycle of simulation parametrization and analysis. Specifically, a novel visualization supports decision making by ranking simulated lighting configurations with regard to a weight-based prioritization of objectives that considers both spatial and non-spatial characteristics. In the spatial domain, novel concepts support a detailed comparison of illumination scenarios. We demonstrate LiteVis using a real-world use case and report qualitative feedback of lighting designers. This feedback indicates that LiteVis successfully supports lighting designers to achieve key tasks more efficiently and with greater certainty.

Rendering view-dependent, glossy surfaces to increase the realism in real-time applications is a computationally complex task, that can only be performed by applying some approximations--especially when immediate changes in the scene in terms of material settings and object placement are a necessity. The use of environment maps is a common approach to this problem, but implicates performance problems due to costly pre-filtering steps or expensive sampling. We, therefore, introduce a regular sampling scheme for environment maps that relies on an efficient MIP-map-based filtering step, and minimizes the number of necessary samples for creating a convincing real-time rendering of glossy BRDF materials.

We propose a new method for the fast computation of light maps using a many-light global-illumination solution. A complete scene can be light mapped on the order of seconds to minutes, allowing fast and consistent previews for editing or even generation at loading time. In our method, virtual point lights are clustered into a set of virtual polygon lights, which represent a compact description of the illumination in the scene. The actual light-map generation is performed directly on the GPU. Our approach degrades gracefully, avoiding objectionable artifacts even for very short computation times.

We present a system to evaluate and improve visual guidance systems and signage for pedestrians inside large buildings. Given a 3D model of an actual building we perform agent-based simulations mimicking the decision making process and navigation patterns of pedestrians trying to find their way to predefined locations. Our main contribution is to enable agents to base their decisions on realistic threedimensional visibility and occlusion cues computed from the actual building geometry with added semantic annotations (e.g. meaning of signs, or purpose of inventory), as well as an interactive visualization of simulated movement trajectories and accompanying visibility data tied to the underlying 3D model. This enables users of the system to quickly pinpoint and solve problems within the simulation by watching, exploring and understanding emergent behavior inside the building. This insight gained from introspection can in turn inform planning and thus improve the effectiveness of guidance systems.