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Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback., 13 pages, submitted to Computers & Graphics Journal

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.

Scene graphs, as found in many visualization systems are a well-established concept for modeling virtual scenes in computer graphics. State-of-the-art approaches typically issue appropriate draw commands while traversing the graph. Equipped with a functional programming mindset we take a different approach and utilize attribute grammars as a central concept for modeling the problem domain declaratively. Instead of issuing draw commands imperatively, we synthesize first class objects describing appropriate draw commands. In order to make this approach practical in the face of dynamic changes to the scene graph, we utilize incremental evaluation, and thereby avoid repeated evaluation of unchanged parts. Our application prototypically demonstrates how complex systems benefit from domain-specific languages, declarative problem solving and the implications thereof. Besides from being concise and expressive, our solution demonstrates a real-world use case of self-adjusting computation which elegantly extends scene graphs with well-defined reactive semantics and efficient, incremental execution.

In order to simplify shader programming we propose a system to specify composable shaders in a functional way directly in typical implementation languages of modern rendering frameworks. In constrast to existing pipeline shader frameworks, our system exposes a radically simplified pipeline, which we purposefully aligned with our basic intuition of shaders as compositions of per-primitive and per-pixel operations. By programming the shaders in the host language, we additionally remove the complexity of handling different programming languages for shaders and the rest of the framework.