Hierarchical Photon Mapping

Spencer, Ben F.; Jones, Mark W.

doi:10.1109/tvcg.2008.67

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…This is especially helpful for combinations of specular-diffuse-specular surface interactions, such as light sources enclosed in glass. Spencer and Jones use hierarchical photon maps to speed up caustic generation [17], and relax the photon map to reduce variance [18]. Recently, Dammertz et al [4] have introduced a biased progressive method, which also divides path space to allow for specialized algorithms to compute a solution for each subspace, in a similar fashion to our work.…”

Section: Related Workmentioning

confidence: 92%

Mixing Monte Carlo and progressive rendering for improved global illumination

2012

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In this paper, we seek to eliminate the noise caused by caustic paths during progressive Monte Carlo path tracing. We employ a filtering strategy over path space, handling each subspace using specialized derivations of path tracing and progressive photon mapping. Evaluating diffuse paths with path tracing allows the use of sample stratification over both pixels and the image as a whole, whilst sharp detailed caustics are produced using progressive photon mapping. This is an efficient, low noise progressive algorithm with vanishing bias combining the advantages of both Monte Carlo methods, and particle tracing.

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Section: Related Workmentioning

confidence: 92%

Mixing Monte Carlo and progressive rendering for improved global illumination

2012

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“…We instead fit to the photon density by progressively refining both the positions and other parameters of an anisotropic Gaussian mixture, providing reduced variance and faster rendering from arbitrary viewpoints. Our work has a number of similarities to both hierarchical photon mapping representations [CB04,SJ09a] which provides LOD for final gather, and photon relaxation [SJ09b] which iteratively moves photon positions to reduce variance. By using Gaussian mixtures we obtain both of these benefits while drawing upon a set of principled and well-established fitting techniques.…”

Section: Related Workmentioning

confidence: 99%

Progressive Expectation‐Maximization for Hierarchical Volumetric Photon Mapping

Jakob

Regg

Jarosz

2011

Computer Graphics Forum

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Beam Radiance Estimation [Jarosz et al. 2008] Our Method 917K Photons (13 + 268 = 281 s) 917K Photons (13 + 268 = 281 s) Per-Pixel Render-time Per-Pixel Render-time 4K Gaussian fit (54 + 71 = 125 s) 4K Gaussian fit (54 + 71 = 125 s) Per-Pixel Render-time Per-Pixel Render-time Figure 1: The beam radiance estimate (left) finds all photons along camera rays, which is a performance bottleneck for this CARS scene due to high volumetric depth complexity and many photons (917K). Our method (right) fits a hierarchical, anisotropic Gaussian mixture model to the photons and can render this scene faster, and with higher quality using only (4K) Gaussian components. The listed times denote the costs of the preprocessing stage (including photon tracing and hierarchy construction), as well as the final rendering stage, respectively. AbstractState-of-the-art density estimation methods for rendering participating media rely on a dense photon representation of the radiance distribution within a scene. A critical bottleneck of such kernel-based approaches is the excessive number of photons that are required in practice to resolve fine illumination details, while controlling the amount of noise. In this paper, we propose a parametric density estimation technique that represents radiance using a hierarchical Gaussian mixture. We efficiently obtain the coefficients of this mixture using a progressive and accelerated form of the Expectation-Maximization algorithm. After this step, we are able to create noise-free renderings of high-frequency illumination using only a few thousand Gaussian terms, where millions of photons are traditionally required. Temporal coherence is trivially supported within this framework, and the compact footprint is also useful in the context of real-time visualization. We demonstrate a hierarchical ray tracing-based implementation, as well as a fast splatting approach that can interactively render animated volume caustics.

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“…Spencer et al [SJ09] use a hierarchical data structure to cluster photon flux data, thus allowing estimate areas of an arbitrary size at a near-constant query cost. Relative photon density per unit area is adjusted by controlling the depth to which the balanced kd-tree is traversed.…”

Section: Bias and Noise Reduction In Photon Density Estimationmentioning

confidence: 99%

Into the Blue: Better Caustics through Photon Relaxation

Spencer¹,

Jones²

2009

Computer Graphics Forum

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The photon mapping method is one of the most popular algorithms employed in computer graphics today. However, obtaining good results is dependent on several variables including kernel shape and bandwidth, as well as the properties of the initial photon distribution. While the photon density estimation problem has been the target of extensive research, most algorithms focus on new methods of optimising the kernel to minimise noise and bias. In this paper we break from convention and propose a new approach that directly redistributes the underlying photons. We show that by relaxing the initial distribution into one with a blue noise spectral signature we can dramatically reduce background noise, particularly in areas of uniform illumination. In addition, we propose an efficient heuristic to detect and preserve features and discontinuities. We then go on to demonstrate how reconfiguration also permits the use of very low bandwidth kernels, greatly improving render times whilst reducing bias.

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Hierarchical Photon Mapping

Cited by 14 publications

References 31 publications

Mixing Monte Carlo and progressive rendering for improved global illumination

Mixing Monte Carlo and progressive rendering for improved global illumination

Progressive Expectation‐Maximization for Hierarchical Volumetric Photon Mapping

Into the Blue: Better Caustics through Photon Relaxation

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