Temporal and spatial anti-aliasing for rendering reflections on water waves

Podee, Namo; Max, Nelson; Iwasaki, Kei; Dobashi, Yoshinori

doi:10.1007/s41095-021-0204-1

Cited by 3 publications

(1 citation statement)

References 22 publications

(27 reference statements)

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“…References [1][2][3] elaborate on the technical methods involved in temporal anti-aliasing and the related issues involved. Podee N et al [4] also used TAA to solve the scintillation problem of water wave reflection. The Spatiotemporal Variance-guided Filtering [16] (SVGF) proposed by Schied et al utilizes spatiotemporal filtering to remove noise, which has many similarities with temporal anti-aliasing, and also shows that temporal anti-aliasing can also be used for image denoising.…”

Section: Temporal Anti-aliasingmentioning

confidence: 99%

Edge temporal anti-aliasing

Xu,

Luo,

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Temporal Anti-Aliasing (TAA) is a popular method for eliminating temporal aliasing problems. However, the images simply processed by TAA become blurred and lose some details. In this paper, an improved TAA algorithm named Edge Temporal Anti-Aliasing (ETAA), is proposed. A time iterative edge detection method is designed to enhance the detection accuracy of pixels with temporal aliasing and spatial aliasing. These pixels are blurred and blended, and other pixels are replaced with the pixels of the current frame. Furthermore, an approximate minimum filter is used to eliminate the flickering phenomenon of high-energy noise. Compared to TAA, ETAA outperforms TAA in terms of image detail preservation when the rendering camera moves. Meanwhile, the time cost of proposed method also can satisfy the requirement of real-time rendering. Experiment results show that ETAA can effectively and quickly eliminate the time aliasing when the camera moves, and achieves a better trade-off in flicker, ghosting and blurring.

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Section: Temporal Anti-aliasingmentioning

confidence: 99%

Edge temporal anti-aliasing

Xu,

Luo,

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Dynamic ocean inverse modeling based on differentiable rendering

Xie,

Gao,

Hou

et al. 2024

Comp. Visual Media

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Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation. To bridge the technical gap between virtual and real environments, we focus on the inverse modeling and reconstruction of visually consistent and property-verifiable oceans, taking advantage of deep learning and differentiable physics to learn geometry and constitute waves in a self-supervised manner. First, we infer hierarchical geometry using two networks, which are optimized via the differentiable renderer. We extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean model. Then, ocean dynamics can be evolved using the reconstructed wave components. Through extensive experiments, we verify that our new method yields satisfactory results for both geometry reconstruction and wave estimation. Moreover, the new framework has the inverse modeling potential to facilitate a host of graphics applications, such as the rapid production of physically accurate scene animation and editing guided by real ocean scenes.

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