Gaze-contingent rendering and display could help meet the increasing resolution and frame rate demands of modern displays while reducing the required latency, bandwidth, and power. However, it is still unclear how degradation of the peripheral image impacts behavior, particularly for the important task of reading. We examined changes in reading speed with different levels of peripheral degradation, varying the size of the text, foveal region, and sub-sampling kernel.

NumPy is a popular Python library used for performing array-based numerical computations. The canonical implementation of NumPy used by most programmers runs on a single CPU core and only a few operations are parallelized across cores. This restriction to single-node CPU-only execution limits both the size of data that can be processed and the speed with which problems can be solved.

We show how to compute global illumination efficiently in scenes with dynamic objects and lighting. We extend classic irradiance probes to a compact encoding of the full irradiance field in a scene. First, we compute the dynamic irradiance field using an efficient GPU memory layout, geometric ray tracing, and appropriate sampling rates without down-sampling or filtering prohibitively-large spherical textures. Second, we devise a robust filtered irradiance query, using a novel visibility-aware moment-based interpolant.

We present Extreme View Synthesis, a solution for novel view extrapolation that works even when the number of input images is small--as few as two. In this context, occlusions and depth uncertainty are two of the most pressing issues, and worsen as the degree of extrapolation increases. We follow the traditional paradigm of performing depth-based warping and refinement, with a few key improvements. First, we estimate a depth probability volume, rather than just a single depth value for each pixel of the novel view.

Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks. Yet there is a need to lower gaze errors further to enable applications requiring higher quality. Further gains can be achieved by personalizing gaze networks, ideally with few calibration samples. However, over-parameterized neural networks are not amenable to learning from few examples as they can quickly over-fit.

We present a solution to avoid self-intersections in ray tracing that is more robust than current common practices while introducing minimal overhead and requiring no parameter tweaking.

We present a fast and efficient method for intersecting rays with Catmull-Clark subdivision surfaces. It takes advantage of the approximation democratized by OpenSubdiv, in which regular patches are represented by tensor product Bézier surfaces and irregular ones are approximated using Gregory patches. Our algorithm operates solely on the original patch data and can process both patch types simultaneously with only a small amount of control flow divergence.

We compare different methods for sampling from discrete probability distributions and introduce a new algorithm which is especially efficient on massively parallel processors, such as GPUs. The scheme preserves the distribution properties of the input sequence, exposes constant time complexity on the average, and significantly lowers the average number of operations for certain distributions when sampling is performed in a parallel algorithm that requires synchronization afterwards.

Restricting path tracing to a small number of paths per pixel for performance reasons rarely achieves a satisfactory image quality for scenes of interest. However, path space filtering may dramatically improve the visual quality by sharing information across vertices of paths classified as proximate. Unlike screen space-based approaches, these paths neither need to be present on the screen, nor is filtering restricted to the first intersection with the scene.