Filtering approaches for real-time anti-aliasing

Jiménez, Jorge; Gutiérrez, Diego; Yang, Jason; Reshetov, Alexander; Demoreuille, Pete; Berghoff, Tobias; Perthuis, Cedric; Yu, Han; McGuire, Morgan; Lottes, Timothy; Malan, Hugh; Persson, Emil; Andreev, D. A.; Sousa, Tiago

doi:10.1145/2037636.2037642

Cited by 33 publications

(27 citation statements)

References 3 publications

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“…Various post-processing methods have been proposed that perform anti-aliasing by reusing information from adjacent pixels [Reshetov 2009;Lottes 2009;Reshetov 2012;Jimenez et al 2011] or adjacent frames [NVIDIA 2014b], thus saving computation. These methods are typically fast and easy to integrate into a rendering engine, and have become a popular choice for modern video games.…”

Section: Related Workmentioning

confidence: 99%

Decoupled coverage anti-aliasing

Wang

Wyman

et al. 2015

Proceedings of the 7th Conference on High-Performance Graphics

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Figure 1: (left) The CITADEL scene rendered with the proposed Decoupled Coverage Anti-Aliasing (DCAA) method. (right) Insets comparing various algorithms for geometric anti-aliasing. Although 8×MSAA still has visible aliasing in regions with fine-scale geometry (e.g., the tree branches or building detail), our approach produces results of comparable quality to the reference, which is computed by correctly downsampling a 64× larger image computed with 8×MSAA (effectively 512 visibility samples per pixel). AbstractState-of-the-art methods for geometric anti-aliasing in real-time rendering are based on Multi-Sample Anti-Aliasing (MSAA), which samples visibility more than shading to reduce the number of expensive shading calculations. However, for high-quality results the number of visibility samples needs to be large (e.g., 64 samples/pixel), which requires significant memory because visibility samples are usually 24-bit depth values. In this paper, we present Decoupled Coverage Anti-Aliasing (DCAA), which improves upon MSAA by further decoupling coverage from visibility for high-quality geometric anti-aliasing. Our work is based on the previously-explored idea that all fragments at a pixel can be consolidated into a small set of visible surfaces. Although in the past this was only used to reduce the memory footprint of the G-Buffer for deferred shading with MSAA, we leverage this idea to represent each consolidated surface with a 64-bit binary mask for coverage and a single decoupled depth value, thus significantly reducing the overhead for high-quality anti-aliasing. To do this, we introduce new surface merging heuristics and resolve mechanisms to manage the decoupled depth and coverage samples. Our prototype implementation runs in real-time on current graphics hardware, and results in a significant reduction in geometric aliasing with less memory overhead than 8×MSAA for several complex scenes.

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Section: Related Workmentioning

confidence: 99%

Decoupled coverage anti-aliasing

Wang

Wyman

et al. 2015

Proceedings of the 7th Conference on High-Performance Graphics

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“…Since then, many intelligent edge-blur techniques have been proposed, and they are widely used in the game industry, since they are fast and power-efficient. We refer the interested reader to the excellent SIGGRAPH course on this topic by Jimenez et al [2011]. We note that these techniques are not robust to all kinds of scenarios.…”

Section: Previous Workmentioning

confidence: 99%

A ⁴

Barringer

Akenine-Möller

2013

ACM Trans. Graph.

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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.

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“…To get rid of these artifacts, many different methods have been developed, and there is a vast body of research on antialiasing techniques. For more details, see [5] and references therein.…”

Section: Previous Workmentioning

confidence: 99%