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We combine state-of-the-art techniques into a system for high-quality, interactive rendering of participating media. We leverage unbiased volume path tracing with multiple scattering, temporally stable neural denoising and NanoVDB [Museth 2021], a fast, sparse voxel tree data structure for the GPU, to explore what performance and image quality can be obtained for rendering volumetric data. Additionally, we integrate neural adaptive sampling to significantly improve image quality at a fixed sample budget. Our system runs at interactive rates at 1920 × 1080 on a single GPU and produces high quality results for complex dynamic volumes.

We present a novel algorithm for reconstructing high-quality defocus blur from a sparsely sampled light field. Our algorithm builds upon recent developments in the area of sheared reconstruction filters and significantly improves reconstruction quality and performance. While previous filtering techniques can be ineffective in regions with complex occlusion, our algorithm handles such scenarios well by partitioning the input samples into depth layers. These depth layers are filtered independently and then combined together, taking into account inter-layer visibility. We also introduce a new separable formulation of sheared reconstruction filters that achieves real-time preformance on a modern GPU and is more than two orders of magnitude faster than previously published techniques.

Real-time rendering with true motion and defocus blur remains an elusive goal for application developers. In recent years, substantial progress has been made in the areas of rasterization, shading, and reconstruction for stochastic rendering. However, we have yet to see an efficient method for decoupled sampling that can be implemented on current or near-future graphics processors. In this paper, we propose one such algorithm that leverages the capability of modern GPUs to perform unordered memory accesses from within shaders. Our algorithm builds per-pixel primitive lists in canonical shading space. All shading then takes place in a single, non-multisampled forward rendering pass using conservative rasterization. This pass exploits the rasterization and shading hardware to perform shading very efficiently, and only samples that are visible in the final image are shaded. Last, the shading samples are gathered and filtered to create the final image. The input to our algorithm can be generated using a variety of methods, of which we show examples of interactive stochastic and interleaved rasterization, as well as ray tracing.

We propose a powerful hardware architecture for pixel shading, which enables flexible control of shading rates and automatic shading reuse between triangles in tessellated primitives. The main goal is efficient pixel shading for moderately to finely tessellated geometry, which is not handled well by current GPUs. Our method effectively decouples the cost of pixel shading from the geometric complexity. It thereby enables a wider use of tessellation and fine geometry, even at very limited power budgets. The core idea is to shade over small local grids in parametric patch space, and reuse shading for nearby samples. We also support the decomposition of shaders into multiple parts, which are shaded at different frequencies. Shading rates can be locally and adaptively controlled, in order to direct the computations to visually important areas and to provide performance scaling with a graceful degradation of quality. Another important benefit of shading in patch space is that it allows efficient rendering of distribution effects, which further closes the gap between real-time and offline rendering.

Stochastic sampling in time and over the lens is essential to produce photo-realistic images, and it has the potential to revolutionize real-time graphics. In this paper, we take an architectural view of the problem and propose a novel hardware architecture for efficient shading in the context of stochastic rendering. We replace previous caching mechanisms by a sorting step to extract coherence, thereby ensuring that only non-occluded samples are shaded. The memory bandwidth is kept at a minimum by operating on tiles and using new buffer compression methods. Our architecture has several unique benefits not traditionally associated with deferred shading. First, shading is performed in primitive order, which enables late shading of vertex attributes and avoids the need to generate a G-buffer of pre-interpolated vertex attributes. Second, we support state changes, e.g., change of shaders and resources in the deferred shading pass, avoiding the need for a single über-shader. We perform an extensive architectural simulation to quantify the benefits of our algorithm on real workloads.

We present a new technique for importance sampling products of complex functions using wavelets. First, we generalize previous work on wavelet products to higher dimensional spaces and show how this product can be sampled on-the-fly without the need of evaluating the full product. This makes it possible to sample products of high-dimensional functions even if the product of the two functions in itself is too memory consuming. Then, we present a novel hierarchical sample warping algorithm that generates high-quality point distributions, which match the wavelet representation exactly. One application of the new sampling technique is rendering of objects with measured BRDFs illuminated by complex distant lighting --- our results demonstrate how the new sampling technique is more than an order of magnitude more efficient than the best previous techniques.