Tero Karras

Maximizing parallelism in the construction of BVHs, octrees, and k -d trees

A number of methods for constructing bounding volume hierarchies and point-based octrees on the GPU are based on the idea of ordering primitives along a space-filling curve. A major shortcoming with these methods is that they construct levels of the tree sequentially, which limits the amount of parallelism that they can achieve. We present a novel approach that improves scalability by constructing the entire tree in parallel. Our main contribution is an in-place algorithm for constructing binary radix trees, which we use as a building block for other types of trees.

Fast parallel construction of high-quality bounding volume hierarchies

We propose a new massively parallel algorithm for constructing high-quality bounding volume hierarchies (BVHs) for ray tracing. The algorithm is based on modifying an existing BVH to improve its quality, and executes in linear time at a rate of almost 40M triangles/sec on NVIDIA GTX Titan. We also propose an improved approach for parallel splitting of triangles prior to tree construction. Averaged over 20 test scenes, the resulting trees offer over 90% of the ray tracing performance of the best offline construction method (SBVH), while previous fast GPU algorithms offer only about 50%. Compared to state-of-the-art, our method offers a significant improvement in the majority of practical workloads that need to construct the BVH for each frame. On the average, it gives the best overall performance when tracing between 7 million and 60 billion rays per frame. This covers most interactive applications, product and architectural design, and even movie rendering.

Efficient Sparse Voxel Octrees

In this paper, we examine the possibilities of using voxel representations as a generic way for expressing complex and feature-rich geometry on current and future GPUs. We present in detail a compact data structure for storing voxels and an efficient algorithm for performing ray casts using this structure. We augment the voxel data with novel contour information that increases geometric resolution, allows more compact encoding of smooth surfaces, and accelerates ray casts. We also employ a novel normal compression format for storing high-precision object-space normals. Finally, we present a variable-radius postprocess filtering technique for smoothing out blockiness caused by discrete sampling of shading attributes. Based on benchmark results, we show that our voxel representation is competitive with triangle-based representations in terms of ray casting performance, while allowing tremendously greater geometric detail and unique shading information for every voxel. Our voxel codebase is open sourced and available at http://code.google.com/p/efficient-sparse-voxel-octrees/.

Megakernels considered harmful: wavefront path tracing on GPUs

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 us-age 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 compare our performance against the traditional megakernel approach, and demonstrate that the wavefront formulation is much better suited for real-world use cases where multiple complex materials are present in the scene.

High-performance software rasterization on GPUs

In this paper, we implement an efficient, completely software-based graphics pipeline on a GPU. Unlike previous approaches, we obey ordering constraints imposed by current graphics APIs, guarantee hole-free rasterization, and support multisample antialiasing. Our goal is to examine the performance implications of not exploiting the fixed-function graphics pipeline, and to discern which additional hardware support would benefit software-based graphics the most. We present significant improvements over previous work in terms of scalability, performance, and capabilities. Our pipeline is malleable and easy to extend, and we demonstrate that in a wide variety of test cases its performance is within a factor of 2--8x compared to the hardware graphics pipeline on a top of the line GPU. Our implementation is open sourced and available at http://code.google.com/p/cudaraster/

Clipless dual-space bounds for faster stochastic rasterization

We present a novel method for increasing the efficiency of stochastic rasterization of motion and defocus blur. Contrary to earlier approaches, our method is efficient even with the low sampling densities commonly encountered in realtime rendering, while allowing the use of arbitrary sampling patterns for maximal image quality. Our clipless dual-space formulation avoids problems with triangles that cross the camera plane during the shutter interval. The method is also simple to plug into existing rendering systems.

Gradient-domain metropolis light transport

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. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling.

On quality metrics of bounding volume hierarchies

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. Our observations shed light on the increasingly common situation that a supposedly good tree construction algorithm produces trees that are slower to trace than expected. We also note that the trees constructed using greedy top-down algorithms are consistently faster to trace than SAH indicates and are also more SIMD-friendly than competing approaches.

Two methods for fast ray-cast ambient occlusion

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.

Audio-driven facial animation by joint end-to-end learning of pose and emotion

We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence.