2022
DOI: 10.1016/j.cag.2021.10.010
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An integrative view of foveated rendering

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Cited by 20 publications
(20 citation statements)
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“…Decrease α, the pixel density of points close to the fixation point increases, and the pixel density of points far away from the fixation point decreases. By modifying the two parameters of the kernel function, the pixel point allocation scheme, that is, most suitable for the distribution of human visual cells can be obtained according to various performance requirements (Mohanto et al, 2022). The rendering effect comparison is shown in Figure 7.…”
Section: K(u) σ • U αmentioning
confidence: 99%
“…Decrease α, the pixel density of points close to the fixation point increases, and the pixel density of points far away from the fixation point decreases. By modifying the two parameters of the kernel function, the pixel point allocation scheme, that is, most suitable for the distribution of human visual cells can be obtained according to various performance requirements (Mohanto et al, 2022). The rendering effect comparison is shown in Figure 7.…”
Section: K(u) σ • U αmentioning
confidence: 99%
“…Our method enhances the output of adaptive-resolution foveated rendering techniques [Mohanto et al 2021] by synthesizing novel image details whose spatial frequencies fall into the range of detectable but not resolvable frequencies by the HVS (Figure 3). We show that these details can be synthesized using carefully controlled procedural noise.…”
Section: Methods Overviewmentioning
confidence: 99%
“…Kaplanyan et al [2019] showed an application of neural networks on foveated image reconstruction with very sparse sampling but with a significant computational overhead similar to other neural reconstruction techniques. For a comprehensive survey of foveated rendering methods, please refer to the work of Mohanto et al [2021].…”
Section: Foveated Renderingmentioning
confidence: 99%
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“…Further, we have classified several influential research papers along these dimensions. A similar review is also provided in a concurrent work by Mohanto et al [Mohanto et al 2022] with a different taxonomy of foveated rendering methods.…”
Section: Introductionmentioning
confidence: 97%