Axis-aligned filtering for interactive physically-based diffuse indirect lighting

Mehta, S.A.; Wang, Brandon; Ramamoorthi, Ravi; Durand, Frédo

doi:10.1145/2461912.2461947

Cited by 31 publications

(37 citation statements)

References 50 publications

(61 reference statements)

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“…5D covariance tracing [Belcour et al 2013] uses a covariance representation that is compact and stable, addressing the full 5D (space-angle-time) light field, and focuses on atomic operations to achieve generality. We instead use an image-space axis-aligned filter similar to [Mehta et al 2012;Mehta et al 2013] combined with adaptive sampling for both primary and secondary effects in a single framework.…”

Section: Previous Workmentioning

confidence: 99%

“…To reduce the filtering overhead, more recent work on axis-aligned 1 filtering ( [Mehta et al 2012;Mehta et al 2013]) for soft shadows and indirect illumination proposes a method for adaptive sampling and image-space filtering that has very low overhead and makes fast render-times possible. But these methods are limited to rendering single effects rather than full distribution ray-tracing with multiple effects, and extending them to a higher-dimensional rendering domain is a challenge.…”

Section: Introductionmentioning

confidence: 99%

“…We extend the flatland 2D Fourier analysis of [Egan et al 2011;Mehta et al 2013], to the 3D light-field in a position-lens-light space for direct or position-lens-angle space for indirect. We derive how the light field is bandlimited due to integration over the lens, light, and angle.…”

Section: Introductionmentioning

confidence: 99%

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Factored axis-aligned filtering for rendering multiple distribution effects

et al. 2014

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a) Our Method, 138 rpp, 3.14 sec (b) Equal time, 176 rpp, 3.11 sec (c) Our method, 138 rpp, 3.14 sec (d) Eq. quality, 5390 rpp, 130 sec (e) No factoring, 138 rpp, 3.10 sec defocus filter (pixels) num. primary rays (f) Primary filter and rpp 1 20 1 64 indirect filter (pixels) num. indirect rays (g) Indirect filter and rpp Figure 1: (a) The CHESS scene, with defocus blur, area light direct and indirect illumination, rendered at 900×1024 with an average 138 atomic rays per pixel (rpp), in 3.14 sec on an NVIDIA Titan GPU. We compare to different methods in the insets. Readers are encouraged to zoom into the PDF to examine the noise. The top row inset is a sharp in-focus region while the other two regions are defocus blurred; all insets include noisy direct and indirect illumination. In (b) we compare to equal time stratified Monte Carlo (MC) sampling with 176 rpp; in (c), Our method; and in (d), Equal quality MC with 5390 rpp is 40× slower. One of the key contributions of our method is factoring of texture and irradiance, so that irradiance can be pre-filtered before combining with texture. Without factoring, the defocus filter cannot remove noise for in-focus regions as shown in (e), top inset. In (f) and (g) top row, we show filter size for texture and indirect irradiance. Black pixels indicate that factoring cannot be used and irradiance cannot be pre-filtered. In the bottom row we show number of primary rays and indirect samples respectively. Our method uses separate sampling rates and filters for primary and secondary effects which makes it more effective. AbstractMonte Carlo (MC) ray-tracing for photo-realistic rendering often requires hours to render a single image due to the large sampling rates needed for convergence. Previous methods have attempted to filter sparsely sampled MC renders but these methods have high reconstruction overheads. Recent work has shown fast performance for individual effects, like soft shadows and indirect illumination, using axis-aligned filtering. While some components of light transport such as indirect or area illumination are smooth, they are often multiplied by high-frequency components such as texture, which prevents their sparse sampling and reconstruction.We propose an approach to adaptively sample and filter for simultaneously rendering primary (defocus blur) and secondary (soft shadows and indirect illumination) distribution effects, based on a multi-dimensional frequency analysis of the direct and indirect illumination light fields. We describe a novel approach of factoring texture and irradiance in the presence of defocus blur, which allows for pre-filtering noisy irradiance when the texture is not noisy. Our approach naturally allows for different sampling rates for primary and secondary effects, further reducing the overall ray count. While the theory considers only Lambertian surfaces, we obtain promising results for moderately glossy surfaces. We demonstrate 30× sampling rate reduction compared to equal quality noise-free MC. Combined with a GPU implementation and l...

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Factored axis-aligned filtering for rendering multiple distribution effects

et al. 2014

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“…Their method also converges to reference Monte Carlo solutions as sampling rates increase beyond the minimum required by the analytical analysis. Later, they extended this approach to include diffuse indirect illumination [MWRD13]. Most recently they developed a method to combine primary (depth of field) and secondary effects (soft shadows, diffuse and moderately glossy indirect illumination) in a single framework [MYRD14], again using image space adaptive sampling and axis-aligned filtering.…”

Section: Local Frequency Analysismentioning

confidence: 99%

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Zwicker

Jarosz

Lehtinen

et al. 2015

Computer Graphics Forum

125

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Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between "a priori" methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and "a posteriori" methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

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“…Here we show that the projection algorithm can be extended to handle depth variations including occlusion, and thus can be used as a practical method. If we apply the simple projection algorithm to generate the defocus effect, there will be artifacts due to angular aliasing, and various rendering algorithms can be applied to address them [Lehtinen et al 2011;Mehta et al 2013]. We will concentrate on generating the all-in-focus (extended depth-of-field) image.…”

Section: Extensions For Depth Variationmentioning

confidence: 99%

A Light Transport Framework for Lenslet Light Field Cameras

Liang¹,

Ramamoorthi

2015

ACM Trans. Graph.

Self Cite

101

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Light field cameras capture full spatio-angular information of the light field, and enable many novel photographic and scientific applications. It is often stated that there is a fundamental tradeoff between spatial and angular resolution, but there has been limited understanding of this tradeoff theoretically or numerically. Moreover, it is very difficult to evaluate the design of a light field camera, because a new design is usually reported with its prototype and rendering algorithm, all of which affect resolution.In this paper, we develop a light transport framework for understanding the fundamental limits of light field camera resolution. We first derive the prefiltering model of lenslet-based light field cameras. The main novelty of our model is in considering the full space-angle sensitivity profile of the photosensor-in particular, real pixels have non-uniform angular sensitivity, responding more to light along the optical axis, rather than at grazing angles. We show that the full sensor profile plays an important role in defining the performance of a light field camera. The proposed method can model all existing lenslet-based light field cameras and allows us to compare them in a unified way in simulation, independent of the practical differences between particular prototypes. We further extend our framework to analyze the performance of two rendering methods: the simple projection-based method and the inverse light transport process. We validate our framework with both flatland simulation and real data from the Lytro light field camera.

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Axis-aligned filtering for interactive physically-based diffuse indirect lighting

Cited by 31 publications

References 50 publications

Factored axis-aligned filtering for rendering multiple distribution effects

Factored axis-aligned filtering for rendering multiple distribution effects

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

A Light Transport Framework for Lenslet Light Field Cameras

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