Efficient Shadows for Sampled Environment Maps

Ben-Artzi, Aner; Ramamoorthi, Ravi; Agrawala, Maneesh

doi:10.1080/2151237x.2006.10129211

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Our work is complementary to both photon mapping and multidimensional light cuts, since our sheared filter can be incorporated to select a large set of appropriate shadow rays to share for a given receiver point, further reducing shadow ray casts. Coherence across occluders and receivers has been used [Bala et al 1999;Hart et al 1999;Agrawala et al 2000;Ben-Artzi et al 2006], as well as separating near-and far-field occlusion [Arikan et al 2005]. Blurring sharp ray traced results in image space can also be used to approximate soft shadows and blurry reflections [Robison and Shirley 2009].…”

Section: Frequency Analysis and Reconstruction Methods Oftenmentioning

confidence: 99%

Frequency analysis and sheared filtering for shadow light fields of complex occluders

Egan

Hecht

Durand³

et al. 2011

ACM Trans. Graph.

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ray tracing of soft shadows produced by area lighting and intricate geometries, such as the shadows through plant leaves or arrays of blockers, is a critical challenge. The final image often has relatively smooth shadow patterns, since it integrates over the light source. However, Monte Carlo rendering exhibits considerable noise even at high sample counts because of the large variance of the integrand due to the intricate shadow function. This article develops an efficient diffuse soft shadow technique for mid to far occluders that relies on a new 4D cache and sheared reconstruction filter. For this, we first derive a frequency analysis of shadows for planar area lights and complex occluders. Our analysis subsumes convolution soft shadows for parallel planes as a special case. It allows us to derive 4D sheared filters that enable lower sampling rates for soft shadows. While previous sheared-reconstruction techniques were able primarily to index samples according to screen position, we need to perform reconstruction at surface receiver points that integrate over vastly different shapes in the

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Section: Frequency Analysis and Reconstruction Methods Oftenmentioning

confidence: 99%

Frequency analysis and sheared filtering for shadow light fields of complex occluders

Egan

Hecht

Durand³

et al. 2011

ACM Trans. Graph.

Self Cite

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“…We believe the approach in this article is largely orthogonal to adaptive sampling methods, which can leverage our insights separately at each refinement level. Moreover, shadow coherence methods like Agrawala et al [2000] and Ben-Artzi et al [2006] are independent of the base sampling scheme, and are therefore also likely to benefit from our theoretical analysis.…”

Section: Montementioning

confidence: 98%

“…Greater efficiency can sometimes be obtained with a variety of adaptive sampling techniques [Mitchell 1991;Guo 1998;Hachisuka et al 2008;Overbeck et al 2009]. There are also methods specialized to exploit shadow coherence [Agrawala et al 2000;Ben-Artzi et al 2006], and recent techniques for sheared reconstruction [Egan et al 2011]. While these methods show the promise for great speedups in the future, many of them can sometimes have undesirable artifacts in complex regions, and in some cases involve manual parameter tuning.…”

Section: Montementioning

confidence: 98%

A theory of monte carlo visibility sampling

Ramamoorthi

Anderson²,

Meyer³

et al. 2012

ACM Trans. Graph.

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Soft shadows from area lights are one of the most crucial effects in high-quality and production rendering, but Monte-Carlo sampling of visibility is often the main source of noise in rendered images. Indeed, it is common to use deterministic uniform sampling for the smoother shading effects in direct lighting, so that all of the Monte Carlo noise arises from visibility sampling alone. In this article, we analyze theoretically and empirically, using both statistical and Fourier methods, the effectiveness of different nonadaptive Monte Carlo sampling patterns for rendering soft shadows. We start with a single image scanline and a linear light source, and gradually consider more complex visibility functions at a pixel. We show analytically that the lowest expected variance is in fact achieved by uniform sampling (albeit at the cost of visual banding artifacts). Surprisingly, we show that for two or more discontinuities in the visibility function, a comparable error to uniform sampling is obtained by “uniform jitter” sampling, where a constant jitter is applied to all samples in a uniform pattern (as opposed to jittering each stratum as in standard stratified sampling). The variance can be reduced by up to a factor of two, compared to stratified or quasi-Monte Carlo techniques, without the banding in uniform sampling. We augment our statistical analysis with a novel 2D Fourier analysis across the pixel-light space. This allows us to characterize the banding frequencies in uniform sampling, and gives insights into the behavior of uniform jitter and stratified sampling. We next extend these results to planar area light sources. We show that the best sampling method can vary, depending on the type of light source (circular, Gaussian, or square/rectangular). The correlation of adjacent “light scanlines” in square light sources can reduce the effectiveness of uniform jitter sampling, while the smoother shape of circular and Gaussian-modulated sources preserves its benefits—these findings are also exposed through our frequency analysis. In practical terms, the theory in this article provides guidelines for selecting visibility sampling strategies, which can reduce the number of shadow samples by 20--40%, with simple modifications to existing rendering code.

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“…Exploiting coherence in both emitters and receivers, Ben-Artzi et al [BRA06] speed up visibility tests in scenes with environment maps by both discretizing the environment map and hierarchically sharing visibility between receivers, with the uncertain areas being evaluated using a flooding approach. Wu and Chuang [WC13] introduced VisibilityCluster, which approximates average visibility between light clusters and receiver slices for light sampling.…”

Section: Visibility Approximationmentioning

confidence: 99%

Adaptive Matrix Completion for Fast Visibility Computations with Many Lights Rendering

Wang

Holzschuch

2020

Computer Graphics Forum

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Several fast global illumination algorithms rely on the Virtual Point Lights framework. This framework separates illumination into two steps: first, propagate radiance in the scene and store it in virtual lights, then gather illumination from these virtual lights. To accelerate the second step, virtual lights and receiving points are grouped hierarchically, for example using Multi-Dimensional Lightcuts. Computing visibility between clusters of virtual lights and receiving points is a bottleneck. Separately, matrix completion algorithms reconstruct completely a low-rank matrix from an incomplete set of sampled elements. In this paper, we use adaptive matrix completion to approximate visibility information after an initial clustering step. We reconstruct visibility information using as little as 10 % to 20 % samples for most scenes, and combine it with shading information computed separately, in parallel on the GPU. Overall, our method computes global illumination 3 or more times faster than previous stateof-the-art methods. CCS Concepts • Computing methodologies → Ray tracing; Visibility;

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Efficient Shadows for Sampled Environment Maps

Cited by 12 publications

References 18 publications

Frequency analysis and sheared filtering for shadow light fields of complex occluders

Frequency analysis and sheared filtering for shadow light fields of complex occluders

A theory of monte carlo visibility sampling

Adaptive Matrix Completion for Fast Visibility Computations with Many Lights Rendering

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