Abstract We present a generalization of the scalar importance function employed by Metropolis Light Transport (MLT) and related Markov chain rendering algorithms. Although MLT is known for its user‐designable mutation rules, we demonstrate that its scalar contribution function is similarly programmable in an unbiased manner. Normally, MLT samples light paths with a tendency proportional to their brightness. For a range of scenes, we demonstrate that this importance function is undesirable and leads to poor sampling behaviour. Instead, we argue that simple user‐designable importance functions can concentrate work in transport effects of interest and increase estimator efficiency. Unlike mutation rules, these functions are not encumbered with the calculation of transitional probabilities. We introduce alternative importance functions, which encourage the Markov chain to aggressively pursue sampling goals of interest to the user. In addition, we prove that these importance functions may adapt over the course of a render in an unbiased fashion. To that end, we introduce multi‐stage MLT, a general rendering setting for creating such adaptive functions. This allows us to create a noise‐sensitive MLT renderer whose importance function explicitly targets noise. Finally, we demonstrate that our techniques are compatible with existing Markov chain rendering algorithms and significantly improve their visual efficiency.

The NVIDIA® OptiX™ ray tracing engine is a programmable system designed for NVIDIA GPUs and other highly parallel architectures. The OptiX engine builds on the key observation that most ray tracing algorithms can be implemented using a small set of programmable operations. Consequently, the core of OptiX is a domain-specific just-in-time compiler that generates custom ray tracing kernels by combining user-supplied programs for ray generation, material shading, object intersection, and scene traversal. This enables the implementation of a highly diverse set of ray tracing-based algorithms and applications, including interactive rendering, offline rendering, collision detection systems, artificial intelligence queries, and scientific simulations such as sound propagation. OptiX achieves high performance through a compact object model and application of several ray tracing-specific compiler optimizations. For ease of use it exposes a single-ray programming model with full support for recursion and a dynamic dispatch mechanism similar to virtual function calls.

This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the network's underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.

We present a novel algorithm for the rendering of hard shadows cast by a point light source. The well-known Z-pass method for rasterizing shadow volumes is not always correct. Our algorithm, which we call ZP+, elegantly corrects Z-pass defects. ZP+ takes advantage of triangle strips and the fast culling capabilities of graphics hardware not available to conventional robust methods like Z-fail. While Z-fail can be up to 80% slower than Z-pass, our new method ZP+ is typically less than 10% slower than Z-pass. Finally, we compare the three methods. When a scene is geometry-bound, ZP+ is always faster than Z-fail. We also explain why, in some situations, Z-pass (hence ZP+) is surprisingly slower than Z-fail on more recent graphics hardware.