GPU-Based Selective Sparse Sampling for Interactive High-Fidelity Rendering

Galea, Steven; Debattista, Kurt; Spina, Sandro

doi:10.1109/vs-games.2014.7012159

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…7.2), they are rarely used for solely model-based approaches. An example of such a system has been proposed by Galea et al [GDS14]. They describe a GPU-based selective rendering algorithm for high-quality rasterization.…”

Section: Image Levelmentioning

confidence: 99%

“…Sampling adaptation method by Galea et al [GDS14]. A deferred rendering system decouples direct and indirect illumination components.…”

Section: Model‐based Perceptual Approachesmentioning

confidence: 99%

“…Inpainting computes a dense representation of indirect lighting that is combined with the direct lighting for the final image. Image from Galea et al [GDS14]…”

Section: Model‐based Perceptual Approachesmentioning

confidence: 99%

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Perception-driven Accelerated Rendering

Weier

Stengel

Roth

et al. 2017

Computer Graphics Forum

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Advances in computer graphics enable us to create digital images of astonishing complexity and realism. However, processing resources are still a limiting factor. Hence, many costly but desirable aspects of realism are often not accounted for, including global illumination, accurate depth of field and motion blur, spectral effects, etc. especially in real-time rendering. At the same time, there is a strong trend towards more pixels per display due to larger displays, higher pixel densities or larger fields of view. Further observable trends in current display technology include more bits per pixel (high dynamic range, wider color gamut/fidelity), increasing refresh rates (better motion depiction), and an increasing number of displayed views per pixel (stereo, multi-view, all the way to holographic or lightfield displays). These developments cause significant unsolved technical challenges due to aspects such as limited compute power and bandwidth. Fortunately, the human visual system has certain limitations, which mean that providing the highest possible visual quality is not always necessary. In this report, we present the key research and models that exploit the limitations of perception to tackle visual quality and workload alike. Moreover, we present the open problems and promising future research targeting the question of how we can minimize the effort to compute and display only the necessary pixels while still offering a user full visual experience.

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Section: Image Levelmentioning

confidence: 99%

“…Sampling adaptation method by Galea et al [GDS14]. A deferred rendering system decouples direct and indirect illumination components.…”

Section: Model‐based Perceptual Approachesmentioning

confidence: 99%