LightGuider is a novel guidance-based approach to interactive lighting design, which typically consists of interleaved 3D modeling operations and light transport simulations. Rather than having designers use a trial-and-error approach to match their illumination constraints and aesthetic goals, LightGuider supports the process by simulating potential next modeling steps that can deliver the most significant improvements. LightGuider takes predefined quality criteria and the current focus of the designer into account to visualize suggestions for lighting-design improvements via a specialized provenance tree. This provenance tree integrates snapshot visualizations of how well a design meets the given quality criteria weighted by the designer's preferences. This integration facilitates the analysis of quality improvements over the course of a modeling workflow as well as the comparison of alternative design solutions. We evaluate our approach with three lighting designers to illustrate its usefulness.

Global Illumination is affected by the slightest change in a 3D scene, requiring a complete reevaluation of the distributed light. In cases where real-time algorithms are not applicable due to high demands on the achievable accuracy, this recomputation from scratch results in artifacts like flickering or noise, disturbing the visual appearance and negatively affecting interactive lighting design workflows.

In this work, we propose an interaction-driven approach streamlined to support and improve a wide range of real-time 2D interaction metaphors for arbitrarily large pointclouds based on detected primitive shapes. Rather than performing shape detection as a costly pre-processing step on the entire point cloud at once, a user-controlled interaction determines the region that is to be segmented next. By keeping the size of the region and the number of points small, the algorithm produces meaningful results and therefore feedback on the local geometry within a fraction of a second. We can apply these finding for improved picking and selection metaphors in large point clouds, and propose further novel shape-assisted interactions that utilize this local semantic information to improve the user's workflow.

In workplaces or publicly accessible buildings, escape routes are signposted according to official norms or international standards that specify distances, angles and areas of interest for the positioning of escape-route signs. In homes for the elderly, in which the residents commonly have degraded mobility and suffer from vision impairments caused by age or eye diseases, the specifications of current norms and standards may be insufficient. Quantifying the effect of symptoms of vision impairments like reduced visual acuity on recognition distances is challenging, as it is cumbersome to find a large number of user study participants who suffer from exactly the same form of vision impairments. Hence, we propose a new methodology for such user studies: By conducting a user study in virtual reality (VR), we are able to use participants with normal or corrected sight and simulate vision impairments graphically. The use of standardized medical eyesight tests in VR allows us to calibrate the visual acuity of all our participants to the same level, taking their respective visual acuity into account. Since we primarily focus on homes for the elderly, we accounted for their often limited mobility by implementing a wheelchair simulation for our VR application.

Capturing urban scenes using photogrammetric methods has become an interesting alternative to laser scanning in the past years. For the reconstruction of CAD-ready 3D models, two main types of interactive approaches have become prevalent: One uses the generated 3D point clouds to reconstruct polygonal surfaces, while the other focuses on 2D interaction in the photos to define edges and faces.

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.

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 propose a novel way to efficiently calculate soft shadows in real-time applications by overcoming the high computational effort involved with the complex corresponding visibility estimation each frame: We exploit the temporal coherence prevalent in typical scene movement, making the estimation of a new shadow value only necessary whenever regions are newly disoccluded due to camera adjustment, or the shadow situation changes due to object movement. By extending the typical shadow mapping algorithm by an additional light-weight buffer for the tracking of dynamic scene objects, we can robustly and efficiently detect all screen space fragments that need to be updated, including not only the moving objects themselves, but also the soft shadows they cast. By applying this strategy to the popular Percentage Closer Soft Shadow algorithm (PCSS), we double rendering performance in scenes with both static and dynamic objects -- as prevalent in various 3D game levels -- while maintaining the visual quality of the original approach.

In this article, we introduce a novel reconstruction and modeling pipeline to create polygonal models from unstructured point clouds. We propose an automatic polygonal reconstruction that can then be interactively refined by the user. An initial model is automatically created by extracting a set of RANSAC-based locally fitted planar primitives along with their boundary polygons, and then searching for local adjacency relations among parts of the polygons. The extracted set of adjacency relations is enforced to snap polygon elements together, while simultaneously fitting to the input point cloud and ensuring the planarity of the polygons. This optimization-based snapping algorithm may also be interleaved with user interaction. This allows the user to sketch modifications with coarse and loose 2D strokes, as the exact alignment of the polygons is automatically performed by the snapping. The generated models are coarse, offer simple editing possibilities by design, and are suitable for interactive 3D applications like games, virtual environments, etc. The main innovation in our approach lies in the tight coupling between interactive input and automatic optimization, as well as in an algorithm that robustly discovers the set of adjacency relations.