Abstract We present a hybrid ray tracing system, where the work is divided between the CPU cores and the GPU in an integrated chip, and communication occurs via shared memory. Rays are organized in large packets that can be distributed among the two units as needed. Testing visibility between rays and the scene is mostly performed using an optimized kernel on the GPU, but the CPU can help as necessary. The CPU cores typically handle most or all shading, which makes it easy to support complex appearances. For efficiency, the CPU cores shade whole batches of rays by sorting them on material and shading each material using a vectorized kernel. In addition, we introduce a method to support light paths with arbitrary recursion, such as multiple recursive Whitted‐style ray tracing and adaptive sampling where the result of a ray is examined before sending the next, while still batching up rays for the benefit of GPU‐accelerated traversal and vectorized shading. This allows our system to achieve high rendering performance while maintaining the flexibility to accommodate different rendering algorithms.

While each new generation of processors gets larger caches and more compute power, external memory bandwidth capabilities increase at a much lower pace. Additionally, processors are equipped with wide vector units that require low instruction level divergence to be efficiently utilized. In order to exploit these trends for ray tracing, we present an alternative to traditional depth-first ray traversal that takes advantage of the available cache hierarchy, and provides high SIMD efficiency, while keeping memory bus traffic low. Our main contribution is an efficient algorithm for traversing large packets of rays against a bounding volume hierarchy in a way that groups coherent rays during traversal. In contrast to previous large packet traversal methods, our algorithm allows for individual traversal order for each ray, which is essential for efficient ray tracing. Ray tracing algorithms is a mature research field in computer graphics, and despite this, our new technique increases traversal performance by 36--53%, and is applicable to most ray tracers.

Edge aliasing continues to be one of the most prominent problems in real-time graphics, e.g., in games. We present a novel algorithm that uses shared memory between the GPU and the CPU so that these two units can work in concert to solve the edge aliasing problem rapidly. Our system renders the scene as usual on the GPU with one sample per pixel. At the same time, our novel edge aliasing algorithm is executed asynchronously on the CPU. First, a sparse set of important pixels is created. This set may include pixels with geometric silhouette edges, discontinuities in the frame buffer, and pixels/polygons under user-guided artistic control. After that, the CPU runs our sparse rasterizer and fragment shader, which is parallel and SIMD:ified, and directly accesses shared resources (e.g., render targets created by the GPU). Our system can render a scene with shadow mapping with adaptive anti-aliasing with 16 samples per important pixel faster than the GPU with 8 samples per pixel using multi-sampling anti-aliasing. Since our system consists of an extensive code base, it will be released to the public for exploration and usage.

Computing accurate visibility for thin primitives, such as hair strands, fur, grass, at all scales remains difficult or expensive. To that end, we present an efficient visibility algorithm based on spatial line sampling, and a novel intersection algorithm between line sample planes and Bézier splines with varying thickness. Our algorithm produces accurate visibility both when the projected width of the curve is a tiny fraction of a pixel, and when the projected width is tens of pixels. In addition, we present a rapid resolve procedure that computes final visibility. Using an optimized implementation running on graphics processors, we can render tens of thousands long hair strands with noise-free visibility at near-interactive rates.

We present a novel visibility algorithm for rendering motion blur with per-pixel anti-aliasing. Our algorithm uses a number of line samples over a rectangular group of pixels, and together with the time dimension, a two-dimensional spatio-temporal visibility problem needs to be solved per line sample. In a coarse culling step, our algorithm first uses a bounding volume hierarchy to rapidly remove geometry that does not overlap with the current line sample. For the remaining triangles, we approximate each triangle's depth function, along the line and along the time dimension, with a number of patch triangles. We resolve for the final color using an analytical visibility algorithm with depth sorting, simple occlusion culling, and clipping. Shading is decoupled from visibility, and we use a shading cache for efficient reuse of shaded values. In our results, we show practically noise-free renderings of motion blur with high-quality spatial anti-aliasing and with competitive rendering times. We also demonstrate that our algorithm, with some adjustments, can be used to accurately compute motion blurred ambient occlusion.