Ambient occlusion has proven to be a useful tool for producing realistic images, both in offline rendering and interactive applications. In production rendering, ambient occlusion is typically computed by casting a large number of short shadow rays from each visible point, yielding unparalleled quality but long rendering times. Interactive applications typically use screen-space approximations which are fast but suffer from systematic errors due to missing information behind the nearest depth layer.

Subpixel Reconstruction Antialiasing (SRAA) combines single-pixel (1x) shading with subpixel visibility to create antialiased images without increasing the shading cost. SRAA targets deferred-shading renderers, which cannot use multisample antialiasing. SRAA operates as a post-process on a rendered image with superresolution depth and normal buffers, so it can be incorporated into an existing renderer without modifying the shaders.

This paper extends the stochastic transparency algorithm that models partial coverage to also model wavelength-varying transmission. It then applies this to the problem of casting shadows between any combination of opaque, colored transmissive, and partially covered (i.e., alpha-matted) surfaces in a manner compatible with existing hardware shadow mapping techniques. Colored Stochastic Shadow Maps have a similar resolution and performance profile to traditional shadow maps, however they require a wider filter in colored areas to reduce hue variation.

Stochastic renderers produce unbiased but noisy images of scenes that include the advanced camera effects of motion and defocus blur and possibly other effects such as transparency. We present a simple algorithm that selectively adds bias in the form of image space blur to pixels that are unlikely to have high frequency content in the final image. For each pixel, we sweep once through a fixed neighborhood of samples in front to back order, using a simple accumulation scheme.

It is usually difficult to resolve the fine details of turbulent flows, especially when targeting real-time applications. We present a novel, scalable turbulence method that uses a realistic energy model and an efficient particle representation that allows for the accurate and robust simulation of small-scale detail. We compute transport of turbulent energy using a complete two-equation k-epsilon model with accurate production terms that allows us to capture anisotropic turbulence effects, which integrate smoothly into the base flow.

This paper proposes a massively parallel hardware architecture for efficient tracing of incoherent rays, e.g. for global illumination. The general approach is centered around hierarchical treelet subdivision of the acceleration structure and repeated queueing/postponing of rays to reduce cache pressure. We describe a heuristic algorithm for determining the treelet subdivision, and show that our architecture can reduce the total memory bandwidth requirements by up to 90% in difficult scenes.

We present HLBVH and SAH-optimized HLBVH, two high performance BVH construction algorithms targeting real-time ray tracing of dynamic geometry. HLBVH provides a novel hierarchical formulation of the LBVH algorithm [Lauterbach et al 2009] and SAH-optimized HLBVH uses a new combination of HLBVH and the greedy surface area heuristic algorithm. These algorithms minimize work and memory bandwidth usage by extracting and exploiting coarse-grained spatial coherence already available in the input meshes.

The NVIDIA(R) OptiX(TM) 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.

Modern GPUs are massively parallel microprocessors that can deliver very high performance for the parallel computations common in science and engineering.