Modeling appealing virtual scenes is an elaborate and time-consuming task, requiring not only training and experience, but also powerful modeling tools providing the desired functionality to the user. In this paper, we describe a modeling approach using signed distance functions as an underlying representation for objects, handling both conventional and complex surface manipulations. Scenes defined by signed distance functions can be stored compactly and rendered directly in real-time using sphere tracing. Hence, we are capable of providing an interactive application with immediate visual feedback for the artist, which is a crucial factor for the modeling process. Moreover, dealing with underlying mathematical operations is not necessary on the user level. We show that fundamental aspects of traditional modeling can be directly transferred to this novel kind of environment, resulting in an intuitive application behavior, and describe modeling operations which naturally benefit from implicit representations. We show modeling examples where signed distance functions are superior to explicit representations, but discuss the limitations of this approach as well.

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In this paper, we extend the concept of pre‐filtered shadow mapping to stochastic rasterization, enabling real‐time rendering of soft shadows from planar area lights. Most existing soft shadow mapping methods lose important visibility information by relying on pinhole renderings from an area light source, providing plausible results only for small light sources. Since we sample the entire 4D shadow light field stochastically, we are able to closely approximate shadows of large area lights as well. In order to efficiently reconstruct smooth shadows from this sparse data, we exploit the analogy of soft shadow computation to rendering defocus blur, and introduce a multiplane pre‐filtering algorithm. We demonstrate how existing pre‐filterable approximations of the visibility function, such as variance shadow mapping, can be extended to four dimensions within our framework.

In the context of geometric acoustic simulation, one of the more perceptually important yet difficult to simulate acoustic effects is diffraction, a phenomenon that allows sound to propagate around obstructions and corners. A significant bottleneck in real-time simulation of diffraction is the enumeration of high-order diffraction propagation paths in scenes with complex geometry (e.g. highly tessellated surfaces). To this end, we present a dynamic geometric diffraction approach that consists of an extensive mesh preprocessing pipeline and complementary runtime algorithm. The preprocessing module identifies a small subset of edges that are important for diffraction using a novel silhouette edge detection heuristic. It also extends these edges with planar diffraction geometry and precomputes a graph data structure encoding the visibility between the edges. The runtime module uses bidirectional path tracing against the diffraction geometry to probabilistically explore potential paths between sources and listeners, then evaluates the intensities for these paths using the Uniform Theory of Diffraction. It uses the edge visibility graph and the A* pathfinding algorithm to robustly and efficiently find additional high-order diffraction paths. We demonstrate how this technique can simulate 10th-order diffraction up to 568 times faster than the previous state of the art, and can efficiently handle large scenes with both high geometric complexity and high numbers of sources.CCS Concepts: • Computing methodologies → Real-time simulation; Ray tracing; Mesh geometry models.