Results

**1 - 3**of**3**### High Performance Graphics (2014) Jonathan Ragan-Kelley and Ingo Wald (Editors) Streaming G-Buffer Compression for Multi-Sample Anti-Aliasing

"... Figure 1: Our streaming compression algorithm reduces the memory usage and shading costs associated with multi-sample anti-aliasing (MSAA) coupled to deferred shading. Here, a scene rendered with our algorithm using 8x MSAA (left) reduces memory usage by 50 % and total running time by 30 % when comp ..."

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Figure 1: Our streaming compression algorithm reduces the memory usage and shading costs associated with multi-sample anti-aliasing (MSAA) coupled to deferred shading. Here, a scene rendered with our algorithm using 8x MSAA (left) reduces memory usage by 50 % and total running time by 30 % when compared to an optimized deferred shading implementation. In most cases we shade once per pixel (right) even when multiple geometric primitives cover it. 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. 1.

### Aggregate G-Buffer Anti-Aliasing

"... Figure 1: Large image rendered with Agregate G-Buffer Anti-Aliasing (AGAA). The AGAA results (red outlines) shade only twice per pixel, give comparable results to the MSAA reference image shaded eight times per pixel, and use 33 % less memory. AGAA reduces aliasing by prefiltering the scene’s sub-pi ..."

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Figure 1: Large image rendered with Agregate G-Buffer Anti-Aliasing (AGAA). The AGAA results (red outlines) shade only twice per pixel, give comparable results to the MSAA reference image shaded eight times per pixel, and use 33 % less memory. AGAA reduces aliasing by prefiltering the scene’s sub-pixel geometric detail (foliage, thin railings, etc.) into an aggregate G-buffer that models the distribution of geometry projecting into each pixel. We present Aggregate G-Buffer Anti-Aliasing (AGAA), a new technique for efficient anti-aliased deferred rendering of complex geometry using modern graphics hardware. In geometrically com-plex situations, where many surfaces intersect a pixel, current ren-dering systems shade each contributing surface at least once per pixel. As the sample density and geometric complexity increase, the shading cost becomes prohibitive for real-time rendering. Under deferred shading, so does the required framebuffer memory. AGAA uses the rasterization pipeline to generate a compact, pre-filtered geometric representation inside each pixel. We then shade this at a fixed rate, independent of geometric complexity. By decoupling shading rate from geometric sampling rate, the algorithm reduces the storage and bandwidth costs of a geometry buffer, and allows scaling to high visibility sampling rates for anti-aliasing. AGAA with 2 aggregate surfaces per-pixel generates results comparable to 8x MSAA, but requires 30 % less memory (45 % savings for 16x MSAA), and is up to 1.3x faster.

### Intel Labs

"... Post-processing antialiasing methods are well suited for deferred shading because they decouple antialiasing from the rest of graphics pipeline. In morphological methods, the final image is filtered with a data-dependent filter. The filter coefficients are computed by analyzing the non-local neighbo ..."

Abstract
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Post-processing antialiasing methods are well suited for deferred shading because they decouple antialiasing from the rest of graphics pipeline. In morphological methods, the final image is filtered with a data-dependent filter. The filter coefficients are computed by analyzing the non-local neighborhood of each pixel. Though very simple and efficient, such methods have intrinsic quality limitations due to spatial undersampling and temporal aliasing. We explore an alternative formulation in which filter coefficients are computed locally for each pixel by supersampling geometry, while shading is still done only once per pixel. During pre-processing, each geometric subsample is converted to a single bit indicating whether the subsample is different from the central one. The ensuing binary mask is then used in the post-processing step to retrieve filter coefficients, which were precomputed for all possible masks. For a typical 8 subsamples, it results in a sub-millisecond performance, while improving the image quality by about 10 dB. To compare subsamples, we use a novel symmetric angular measure, which has a simple geometric interpretation. We propose to use this measure in a variety of applications that assess the difference between geometric samples (rendering, mesh simplification, geometry encoding, adaptive tessellation).