Browsing by Author "Nowrouzezahrai, Derek"

Now showing 1 - 10 of 10
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    Dynamic Diffuse Global Illumination Resampling
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Majercik, Zander; Müller, Thomas; Keller, Alexander; Nowrouzezahrai, Derek; McGuire, Morgan; Hauser, Helwig and Alliez, Pierre
    Interactive global illumination remains a challenge in radiometrically and geometrically complex scenes. Specialized sampling strategies are effective for specular and near‐specular transport because the scattering has relatively low directional variance per scattering event. In contrast, the high variance from transport paths comprising multiple rough glossy or diffuse scattering events remains notoriously difficult to resolve with a small number of samples. We extend unidirectional path tracing to address this by combining screen‐space reservoir resampling and sparse world‐space probes, significantly improving sample efficiency for transport contributions that terminate on diffuse scattering events. Our experiments demonstrate a clear improvement—at equal time and equal quality—over purely path traced and purely probe‐based baselines. Moreover, when combined with commodity denoisers, we are able to interactively render global illumination in complex scenes.
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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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    Impulse Responses for Precomputing Light from Volumetric Media
    (The Eurographics Association, 2019) Dubouchet, Adrien; Sloan, Peter-Pike; Jarosz, Wojciech; Nowrouzezahrai, Derek; Boubekeur, Tamy and Sen, Pradeep
    Modern interactive rendering can rely heavily on precomputed static lighting on surfaces and in volumes. Scattering from volumetric media can be similarly treated using precomputation, but transport from volumes onto surfaces is typically ignored here. We propose a compact, efficient method to simulate volume-to-surface transport during lighting precomputation . We leverage a novel model of the spherical impulse response of light scattered (and attenuated) in volumetric media to simulate light transport from volumes onto surfaces with simple precomputed lookup tables. These tables model the impulse response as a function of distance and angle to the light and surfaces. We then remap the impulse responses to media with arbitrary, potentially heterogeneous scattering parameters and various phase functions. Moreover, we can compose our impulse response model to treat multiple scattering events in the volume (arriving at surfaces). We apply our method to precomputed volume-to-surface light transport in complex scenes, generating results indistinguishable from ground truth simulations. Our tables allow us to precompute volume-to-surface transport orders of magnitude faster than even an optimized path tracing-based solution would.
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    Local Bases for Model-reduced Smoke Simulations
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Mercier, Olivier; Nowrouzezahrai, Derek; Panozzo, Daniele and Assarsson, Ulf
    We present a flexible model reduction method for simulating incompressible fluids.We derive a novel vector field basis composed of localized basis flows which have simple analytic forms and can be tiled on regular lattices, avoiding the use of complicated data structures or neighborhood queries. Local basis flow interactions can be precomputed and reused to simulate fluid dynamics on any simulation domain without additional overhead. We introduce heuristic simulation dynamics tailored to our basis and derived from a projection of the Navier-Stokes equations to produce physically plausible motion, exposing intuitive parameters to control energy distribution across scales. Our basis can adapt to curved simulation boundaries, can be coupled with dynamic obstacles, and offers simple adjustable trade-offs between speed and visual resolution.
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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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    Subspace Neural Physics: Fast Data-Driven Interactive Simulation
    (ACM, 2019) Holden, Daniel; Duong, Bang Chi; Datta, Sayantan; Nowrouzezahrai, Derek; Batty, Christopher and Huang, Jin
    Data-driven methods for physical simulation are an attractive option for interactive applications due to their ability to trade precomputation and memory footprint in exchange for improved runtime performance. Yet, existing data-driven methods fall short of the extreme memory and performance constraints imposed by modern interactive applications like AAA games and virtual reality. Here, performance budgets for physics simulation range from tens to hundreds of micro-seconds per frame, per object. We present a data-driven physical simulation method that meets these constraints. Our method combines subspace simulation techniques with machine learning which, when coupled, enables a very efficient subspace-only physics simulation that supports interactions with external objects - a longstanding challenge for existing subspace techniques. We also present an interpretation of our method as a special case of subspace Verlet integration, where we apply machine learning to efficiently approximate the physical forces of the system directly in the subspace. We propose several practical solutions required to make effective use of such a model, including a novel training methodology required for prediction stability, and a GPU-friendly subspace decompression algorithm to accelerate rendering.
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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.