We show how to efficiently compute 2D polyhedral bounds of the (elliptic) perspective projection of a 3D sphere that has been clipped to the near plane.

We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes.

Spatially-varying reflectance and small geometric variations play a vital role in the appearance of real-world surfaces. Consequently, robust, automatic capture of such models is highly desirable; however, current systems require either specialized hardware, long capture times, user intervention, or rely heavily on heuristics. We describe an acquisition setup that utilizes only portable commodity hardware (an LCD display, an SLR camera) and contains no moving parts. In particular, a laptop screen can be used for illumination.

Stochastic techniques for rendering indirect illumination suffer from noise due to the variance in the integrand. In this paper, we describe a general reconstruction technique that exploits anisotropy in the light field and permits efficient reuse of input samples between pixels or world-space locations, multiplying the effective sampling rate by a large factor. Our technique introduces visibility-aware anisotropic reconstruction to indirect illumination, ambient occlusion and glossy reflections.

When programming for GPUs, simply porting a large CPU program into an equally large GPU kernel is generally not a good approach. Due to SIMT execution model on GPUs, divergence in control flow carries substantial performance penalties, as does high register usage that lessens the latency-hiding capability that is essential for the high-latency, high-bandwidth memory system of a GPU. In this paper, we implement a path tracer on a GPU using a wavefront formulation, avoiding these pitfalls that can be especially prominent when using materials that are expensive to evaluate.

We present a novel approach to voxelization, based on intersecting the input primitives against intersection targets in the voxel grid. Instead of relying on geometric proximity measures, our approach is topological in nature, i.e., it builds on the connectivity and separability properties of the input and the intersection targets. We discuss voxelization of curves and surfaces in both 2D and 3D, and derive intersection targets that produce voxelizations with various connectivity, separability and thinness properties. The simplicity of our method allows for easy proofs of these properties.

Functional languages provide a solid foundation on which complex optimization passes can be designed to exploit available parallelism in the underlying system. Their mathematical foundations enable high-level optimizations that would be impossible in traditional imperative languages. This makes them uniquely suited for generation of efficient target code for parallel systems, such as multiple Central Processing Units (CPUs) or highly data-parallel Graphics Processing Units (GPUs). Such systems are becoming the mainstream for scientific and ‘desktop’ computing.

We introduce CloudLight, a system for computing indirect lighting in the Cloud to support real-time rendering for interactive 3D applications on a user's local device. CloudLight maps the traditional graphics pipeline onto a distributed system. That differs from a single-machine renderer in three fundamental ways. First, the mapping introduces potential asymmetry between computational resources available at the Cloud and local device sides of the pipeline.

We propose near-eye light field displays that enable thin, lightweight head-mounted displays (HMDs) capable of presenting nearly correct convergence, accommodation, binocular disparity, and retinal defocus depth cues. Sharp images are depicted by out-of-focus elements by synthesizing light fields corresponding to virtual objects within a viewer's natural accommodation range. We formally assess the capabilities of microlens arrays to achieve practical near-eye light field displays.

The surface area heuristic (SAH) is widely used as a predictor for ray tracing performance, and as a heuristic to guide the construction of spatial acceleration structures. We investigate how well SAH actually predicts ray tracing performance of a bounding volume hierarchy (BVH), observe that this relationship is far from perfect, and then propose two new metrics that together with SAH almost completely explain the measured performance.