We introduce an energy efficient time-sharing pyramid pipeline architecture designed for multi-resolution image analysis in mobile computer vision. The time-sharing pipeline efficiently reduces the off-chip memory traffic by re-organizing the data storage and processing order of an image pyramid. We build a parameterized image pyramid hardware generator and successfully evaluate the overall pyramid design space.

Voxelized shadow volumes provide a discretized view-dependent representation of shadow volumes, but are limited to point or directional lights. We extend them to allow dynamic volumetric visibility from area light sources using imperfect shadow volumes. We show a coarser visibility sampling suffices for area lights. Combining this coarser resolution with a parallel shadow volume construction enables interactive rendering of dynamic volumetric shadows from area lights in homogeneous single-scattering media, at under 4x the cost of hard volumetric shadows.

We provide three analytical fits to the CIE x, y, and z color matching curves, commonly used in predictive and spectral renderers as an intermediate between light spectra and RGB colors. Any can replace the standard CIE curves, which come tabulated. Using tabulated curves can introduce typos, encourage crude simplifying approximations, or add opportunities to download curves from sources featuring inconsistent or incorrect data. Our analytic fits are simple to implement and verify.

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.