Browsing by Author "Gruson, Adrien"

Now showing 1 - 7 of 7
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    An Efficient Transport Estimator for Complex Layered Materials
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Gamboa, Luis E.; Gruson, Adrien; Nowrouzezahrai, Derek; Panozzo, Daniele and Assarsson, Ulf
    Layered materials capture subtle, realistic reflection behaviors that traditional single-layer models lack. Much of this is due to the complex subsurface light transport at the interfaces of - and in the media between - layers. Rendering with these materials can be costly, since we must simulate these transport effects at every evaluation of the underlying reflectance model. Rendering an image requires thousands of such evaluations, per pixel. Recent work treats this complexity by introducing significant approximations, requiring large precomputed datasets per material, or simplifying the light transport simulations within the materials. Even the most effective of these methods struggle with the complexity induced by high-frequency variation in reflectance parameters and micro-surface normal variation, as well as anisotropic volumetric scattering between the layer interfaces. We present a more efficient, unbiased estimator for light transport in such general, complex layered appearance models. By conducting an analysis of the types of transport paths that contribute most to the aggregate reflectance dynamics, we propose an effective and unbiased path sampling method that reduces variance in the reflectance evaluations. Our method additionally supports reflectance importance sampling, does not rely on any precomputation, and so integrates readily into existing renderers. We consistently outperform the state-of-the-art by ~2-6x in equal-quality (i.e., equal error) comparisons.
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    Practical Product Path Guiding Using Linearly Transformed Cosines
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Diolatzis, Stavros; Gruson, Adrien; Jakob, Wenzel; Nowrouzezahrai, Derek; Drettakis, George; Dachsbacher, Carsten and Pharr, Matt
    Path tracing is now the standard method used to generate realistic imagery in many domains, e.g., film, special effects, architecture etc. Path guiding has recently emerged as a powerful strategy to counter the notoriously long computation times required to render such images. We present a practical path guiding algorithm that performs product sampling, i.e., samples proportional to the product of the bidirectional scattering distribution function (BSDF) and incoming radiance. We use a spatial-directional subdivision to represent incoming radiance, and introduce the use of Linearly Transformed Cosines (LTCs) to represent the BSDF during path guiding, thus enabling efficient product sampling. Despite the computational efficiency of LTCs, several optimizations are needed to make our method cost effective. In particular, we show how we can use vectorization, precomputation, as well as strategies to optimize multiple importance sampling and Russian roulette to improve performance. We evaluate our method on several scenes, demonstrating consistent improvement in efficiency compared to previous work, especially in scenes with significant glossy inter-reflection.
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    Practical Product Sampling for Single Scattering in Media
    (The Eurographics Association, 2021) Villeneuve, Keven; Gruson, Adrien; Georgiev, Iliyan; Nowrouzezahrai, Derek; Bousseau, Adrien and McGuire, Morgan
    Efficient Monte-Carlo estimation of volumetric single scattering remains challenging due to various sources of variance, including transmittance, phase-function anisotropy, geometric cosine foreshortening, and squared-distance fall-off. We propose several complementary techniques to importance sample each of these terms and their product. First, we introduce an extension to equi-angular sampling to analytically account for the foreshortening at point-normal emitters. We then include transmittance and phase function via Taylor-series expansion and/or warp composition. Scaling to complex mesh emitters is achieved through an adaptive tree-splitting scheme. We show improved performance over state-of-the-art baselines in a diversity of scenarios.
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    Scalable Virtual Ray Lights Rendering for Participating Media
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Vibert, Nicolas; Gruson, Adrien; Stokholm, Heine; Mortensen, Troels; Jarosz, Wojciech; Hachisuka, Toshiya; Nowrouzezahrai, Derek; Boubekeur, Tamy and Sen, Pradeep
    Virtual ray lights (VRL) are a powerful representation for multiple-scattered light transport in volumetric participating media. While efficient Monte Carlo estimators can importance sample the contribution of a VRL along an entire sensor subpath, render time still scales linearly in the number of VRLs. We present a new scalable hierarchial VRL method that preferentially samples VRLs according to their image contribution. Similar to Lightcuts-based approaches, we derive a tight upper bound on the potential contribution of a VRL that is efficient to compute. Our bound takes into account the sampling probability densities used when estimating VRL contribution. Ours is the first such upper bound formulation, leading to an efficient and scalable rendering technique with only a few intuitive user parameters. We benchmark our approach in scenes with many VRLs, demonstrating improved scalability compared to existing state-of-the-art techniques.
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    Single-pass Stratified Importance Resampling
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Ciklabakkal, Ege; Gruson, Adrien; Georgiev, Iliyan; Nowrouzezahrai, Derek; Hachisuka, Toshiya; Ghosh, Abhijeet; Wei, Li-Yi
    Resampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired target. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space-filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling-based rendering problems.
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    Stratified Markov Chain Monte Carlo Light Transport
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Gruson, Adrien; West, Rex; Hachisuka, Toshiya; Panozzo, Daniele and Assarsson, Ulf
    Markov chain Monte Carlo (MCMC) sampling is a powerful approach to generate samples from an arbitrary distribution. The application to light transport simulation allows us to efficiently handle complex light transport such as highly occluded scenes. Since light transport paths in MCMC methods are sampled according to the path contributions over the sampling domain covering the whole image, bright pixels receive more samples than dark pixels to represent differences in the brightness. This variation in the number of samples per pixel is a fundamental property of MCMC methods. This property often leads to uneven convergence over the image, which is a notorious and fundamental issue of any MCMC method to date. We present a novel stratification method of MCMC light transport methods. Our stratification method, for the first time, breaks the fundamental limitation that the number of samples per pixel is uncontrollable. Our method guarantees that every pixel receives a specified number of samples by running a single Markov chain per pixel. We rely on the fact that different MCMC processes should converge to the same result when the sampling domain and the integrand are the same. We thus subdivide an image into multiple overlapping tiles associated with each pixel, run an independent MCMC process in each of them, and then align all of the tiles such that overlapping regions match. This can be formulated as an optimization problem similar to the reconstruction step for gradient-domain rendering. Further, our method can exploit the coherency of integrands among neighboring pixels via coherent Markov chains and replica exchange. Images rendered with our method exhibit much more predictable convergence compared to existing MCMC methods.
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    A Survey on Gradient-Domain Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Hua, Binh-Son; Gruson, Adrien; Petitjean, Victor; Zwicker, Matthias; Nowrouzezahrai, Derek; Eisemann, Elmar; Hachisuka, Toshiya; Giachetti, Andrea and Rushmeyer, Holly
    Monte Carlo methods for physically-based light transport simulation are broadly adopted in the feature film production, animation and visual effects industries. These methods, however, often result in noisy images and have slow convergence. As such, improving the convergence of Monte Carlo rendering remains an important open problem. Gradient-domain light transport is a recent family of techniques that can accelerate Monte Carlo rendering by up to an order of magnitude, leveraging a gradient-based estimation and a reformulation of the rendering problem as an image reconstruction. This state of the art report comprehensively frames the fundamentals of gradient-domain rendering, as well as the pragmatic details behind practical gradient-domain uniand bidirectional path tracing and photon density estimation algorithms. Moreover, we discuss the various image reconstruction schemes that are crucial to accurate and stable gradient-domain rendering. Finally, we benchmark various gradient-domain techniques against the state-of-the-art in denoising methods before discussing open problems.