Fast Computation of Single Scattering in Participating Media with Refractive Boundaries Using Frequency Analysis

Liang, Yan-Hong; Wang, Beibei; Wang, Lu; Holzschuch, Nicolas

doi:10.1109/tvcg.2019.2909875

Cited by 4 publications

(3 citation statements)

References 17 publications

(55 reference statements)

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“…Recently, Liang et al [29] introduced frequency analysis theory to single scattering computation in PBGI. Since single scattering is usually highfrequency, and a large number of volume samples are required to produce sharp volumetric caustics, they proposed to use covariance tracing of the volume samples and then adjust the kernel size for single scattering computation, resulting in higher quality with fewer volume samples.…”

Section: Point Based Methodsmentioning

confidence: 99%

A survey on rendering homogeneous participating media

Wang

Yan

2021

Comp. Visual Media

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Participating media are frequent in real-world scenes, whether they contain milk, fruit juice, oil, or muddy water in a river or the ocean. Incoming light interacts with these participating media in complex ways: refraction at boundaries and scattering and absorption inside volumes. The radiative transfer equation is the key to solving this problem. There are several categories of rendering methods which are all based on this equation, but using different solutions. In this paper, we introduce these groups, which include volume density estimation based approaches, virtual point/ray/beam lights, point based approaches, Monte Carlo based approaches, acceleration techniques, accurate single scattering methods, neural network based methods, and spatially-correlated participating media related methods. As well as discussing these methods, we consider the challenges and open problems in this research area.

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Section: Point Based Methodsmentioning

confidence: 99%

A survey on rendering homogeneous participating media

Wang

Yan

2021

Comp. Visual Media

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“…Later, a GPU version of the method was implemented, which achieved real-time frame rates with up to 50 million particles [80]. Furthermore, [81] extended the original offline method in single scattering cases by transforming the angle dependent light transport values from RGB-angle space into the frequency domain with a covariance matrix eigenvector representation. They adopted the idea from [82] where surface and participating media events, such as free space transport and reflections, altered the covariance space eigenvectors with an appropriate matrix transformation.…”

Section: Scattering Radiancementioning

confidence: 99%

Real-Time Rendering of Point Clouds With Photorealistic Effects: A Survey

et al. 2022

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Readily available RGB-D cameras in smart phones and improving 3D scanning technologies have made it possible to produce detailed point cloud and point-based models of real world objects even in real time. Rendering such models in high quality and at satisfactory frame rates is needed for realistic extended reality (XR) applications. This publication reviews real-time photorealistic point cloud rendering methods which directly ray trace or rasterize point cloud models, with an emphasis on ray tracing and realtime performance. We found that real-time direct point cloud ray tracing research has been focused on static non-animated content, and thus, open research possibilities include adapting modern dedicated ray tracing hardware for increased performance for animated and live captured scenes, and adding path tracing techniques to increase photorealistic effects in the rendering result. A categorization and discussion on the capabilities of state-of-the-art photorealistic point cloud rendering methods is presented by surveying both real-time and offline methods, which are assumed to become real-time capable with the advances in near-future hardware. Challenges and future trends are derived by comparing different rasterization and ray tracing methods as well as acceleration structures for point clouds in terms of produced rendering effects and speed.

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“…Standard operations on light transport, such as transport in free space or reflection, Fig. 2 The local light field is defined as a 4D function around the central ray (ω), parameterized by two spatial coordinates (δx and δy) and two angular coordinates (δ θ and δ φ ) [18].…”

Section: Light Transport Covariance In Path Spacementioning

confidence: 99%

A detail preserving neural network model for Monte Carlo denoising

Lin

Wang

et al. 2020

Comp. Visual Media

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Monte Carlo based methods such as path tracing are widely used in movie production. To achieve low noise, they require many samples per pixel, resulting in long rendering time. To reduce the cost, one solution is Monte Carlo denoising, which renders the image with fewer samples per pixel (as little as 128) and then denoises the resulting image. Many Monte Carlo denoising methods rely on deep learning: they use convolutional neural networks to learn the relationship between noisy images and reference images, using auxiliary features such as position and normal together with image color as inputs. The network predicts kernels which are then applied to the noisy input. These methods show powerful denoising ability, but tend to lose geometric or lighting details and to blur sharp features during denoising.In this paper, we solve this issue by proposing a novel network structure, a new input feature-light transport covariance from path space-and an improved loss function. Our network separates feature buffers from the color buffer to enhance detail effects. The features are extracted separately and then integrated into a shallow kernel predictor. Our loss function considers perceptual loss, which also improves detail preservation. In addition, we use a light transport covariance feature in path space as one of the features, which helps to preserve illumination details. Our method denoises Monte Carlo path traced images while preserving details much better than previous methods.

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Fast Computation of Single Scattering in Participating Media with Refractive Boundaries Using Frequency Analysis

Cited by 4 publications

References 17 publications

A survey on rendering homogeneous participating media

A survey on rendering homogeneous participating media

Real-Time Rendering of Point Clouds With Photorealistic Effects: A Survey

A detail preserving neural network model for Monte Carlo denoising

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