Marco Salvi

Adaptive transparency

Adaptive transparency is a new solution to order-independent transparency that closely approximates the ground-truth results obtained with A-buffer compositing but, like a Z-buffer, operates in bounded memory and exhibits consistent performance. The key contribution of our method is an adaptively compressed visibility representation that can be efficiently constructed and queried while rendering. The algorithm supports a wide range and combination of transparent geometry (e.g., foliage, windows, hair, and smoke). We demonstrate that adaptive transparency is five to forty times faster than realtime A-buffer implementations, closely matches the image quality, and is both higher quality and faster than other approximate order-independent transparency techniques: stochastic transparency, uniform opacity shadow maps, and Fourier opacity mapping.

Sample distribution shadow maps

This paper introduces Sample Distribution Shadow Maps (SDSMs), a new algorithm for hard and soft-edged shadows that greatly reduces undersampling, oversampling, and geometric aliasing errors compared to other shadow map techniques. SDSMs fall into the space between scene-dependent, variable-performance shadow algorithms and scene-independent, fixed-performance shadow algorithms. They provide a fully automated solution to shadow map aliasing by optimizing the placement and size of a fixed number of Z-partitions using the distribution of the light space samples required by the current frame. SDSMs build on the advantages of current state of the art techniques, including predictable performance and constant memory usage, while removing tedious and ultimately suboptimal parameter tuning. We compare SDSMs to Parallel-Split Shadow Maps (PSSMs, a state of the art Z-partitioning scheme) and show that SDSMs produce higher quality shadows. Finally, we demonstrate that SDSMs outperform PSSMs in a large 2009 game scene at high resolutions, making them suitable for games and other interactive applications.

Adaptive volumetric shadow maps

We introduce adaptive volumetric shadow maps (AVSM), a real‐time shadow algorithm that supports high‐quality shadowing from dynamic volumetric media such as hair and smoke. The key contribution of AVSM is the introduction of a streaming simplification algorithm that generates an accurate volumetric light attenuation function using a small fixed memory footprint. This compression strategy leads to high performance because the visibility data can remain in on‐chip memory during simplification and can be efficiently sampled during rendering. We demonstrate that AVSM compression closely approximates the ground‐truth correct solution and performs competitively to existing real‐time rendering techniques while providing higher quality volumetric shadows.

Coarse pixel shading

We present a novel architecture for flexible control of shading rates in a GPU pipeline, and demonstrate substantially reduced shading costs for various applications. We decouple shading and visibility by restricting and quantizing shading rates to a finite set of screen-aligned grids, leading to simpler and fewer changes to the GPU pipeline compared to alternative approaches. Our architecture introduces different mechanisms for programmable control of the shading rate, which enables efficient shading in several scenarios, e.g., rendering for high pixel density displays, foveated rendering, and adaptive shading for motion and defocus blur. We also support shading at multiple rates in a single pass, which allows the user to compute different shading terms at rates better matching their frequency content.

Layered Light Field Reconstruction for Defocus Blur

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.

Multi-layer alpha blending

We introduce multi-layer alpha blending, a novel solution to real-time order-independent transparency that operates in a single rendering pass and in bounded memory. The main contribution of our method is a new scalable approximation for the compositing equation that makes possible to easily trade off better image quality for more memory and lower performance. We demonstrate improved image quality and performance over previously published methods, while also reducing memory requirements.

Surface based anti-aliasing

We present surface based anti-aliasing (SBAA), a new approach to real-time anti-aliasing for deferred renderers that improves the performance and lowers the memory requirements for anti-aliasing methods that sample sub-pixel visibility. We introduce a novel way of decoupling visibility determination from shading that, compared to previous multi-sampling based approaches, significantly reduces the number of samples stored and shaded per pixel. Unlike post-process anti-aliasing techniques used in conjunction with deferred renderers, SBAA correctly resolves visibility of sub-pixel features, minimizing spatial and temporal artifacts.

Adaptive image space shading for motion and defocus blur

We present a novel anisotropic sampling algorithm for image space shading which builds upon recent advancements in decoupled sampling for stochastic rasterization pipelines. First, we analyze the frequency content of a pixel in the presence of motion and defocus blur. We use this analysis to derive bounds for the spectrum of a surface defined over a two-dimensional and motion-aligned shading space. Second, we present a simple algorithm that uses the new frequency bounds to reduce the number of shaded quads and the size of decoupling cache respectively by 2X and 16X, while largely preserving image detail and minimizing additional aliasing.

Streaming G-buffer compression for multi-sample anti-aliasing

We present a novel lossy compression algorithm for G-buffers that enables deferred shading applications with high visibility sampling rates. Our streaming compression method operates in a single geometry rendering pass with a fixed, but scalable, amount of per pixel memory. We demonstrate reduced memory requirements and improved performance, with minimal impact on image quality.

Design and novel uses of higher-dimensional rasterization

This paper assumes the availability of a very fast higher-dimensional rasterizer in future graphics processors. Working in up to five dimensions, i.e., adding time and lens parameters, it is well-known that this can be used to render scenes with both motion blur and depth of field. Our hypothesis is that such a rasterizer can also be used as a flexible tool for other, less conventional, usage areas, similar to how the two-dimensional rasterizer in contemporary graphics processors has been used for widely different purposes other than the original intent. We show six such examples, namely, continuous collision detection, caustics rendering, higher-dimensional sampling, glossy reflections and refractions, motion blurred soft shadows, and finally multi-view rendering. The insights gained from these examples are used to put together a coherent model for what a future graphics pipeline that supports these and other use cases should look like. Our work intends to provide inspiration and motivation for hardware and API design, as well as continued research in higher-dimensional rasterization and its uses.