Browsing by Author "Subr, Kartic"

Now showing 1 - 5 of 5
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    Analysis of Sample Correlations for Monte Carlo Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Singh, Gurprit; Öztireli, Cengiz; Ahmed, Abdalla G. M.; Coeurjolly, David; Subr, Kartic; Deussen, Oliver; Ostromoukhov, Victor; Ramamoorthi, Ravi; Jarosz, Wojciech; Giachetti, Andrea and Rushmeyer, Holly
    Modern physically based rendering techniques critically depend on approximating integrals of high dimensional functions representing radiant light energy. Monte Carlo based integrators are the choice for complex scenes and effects. These integrators work by sampling the integrand at sample point locations. The distribution of these sample points determines convergence rates and noise in the final renderings. The characteristics of such distributions can be uniquely represented in terms of correlations of sampling point locations. Hence, it is essential to study these correlations to understand and adapt sample distributions for low error in integral approximation. In this work, we aim at providing a comprehensive and accessible overview of the techniques developed over the last decades to analyze such correlations, relate them to error in integrators, and understand when and how to use existing sampling algorithms for effective rendering workflows.
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    Displacement-Correlated XFEM for Simulating Brittle Fracture
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Chitalu, Floyd M.; Miao, Qinghai; Subr, Kartic; Komura, Taku; Panozzo, Daniele and Assarsson, Ulf
    We present a remeshing-free brittle fracture simulation method under the assumption of quasi-static linear elastic fracture mechanics (LEFM). To achieve this, we devise two algorithms. First, we develop an approximate volumetric simulation, based on the extended Finite Element Method (XFEM), to initialize and propagate Lagrangian crack-fronts. We model the geometry of fracture explicitly as a surface mesh, which allows us to generate high-resolution crack surfaces that are decoupled from the resolution of the deformation mesh. Our second contribution is a mesh cutting algorithm, which produces fragments of the input mesh using the fracture surface. We do this by directly operating on the half-edge data structures of two surface meshes, which enables us to cut general surface meshes including those of concave polyhedra and meshes with abutting concave polygons. Since we avoid triangulation for cutting, the connectivity of the resulting fragments is identical to the (uncut) input mesh except at edges introduced by the cut. We evaluate our simulation and cutting algorithms and show that they outperform state-of-the-art approaches both qualitatively and quantitatively.
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    Fourier Analysis of Correlated Monte Carlo Importance Sampling
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Singh, Gurprit; Subr, Kartic; Coeurjolly, David; Ostromoukhov, Victor; Jarosz, Wojciech; Benes, Bedrich and Hauser, Helwig
    Fourier analysis is gaining popularity in image synthesis as a tool for the analysis of error in Monte Carlo (MC) integration. Still, existing tools are only able to analyse convergence under simplifying assumptions (such as randomized shifts) which are not applied in practice during rendering. We reformulate the expressions for bias and variance of sampling‐based integrators to unify non‐uniform sample distributions [importance sampling (IS)] as well as correlations between samples while respecting finite sampling domains. Our unified formulation hints at fundamental limitations of Fourier‐based tools in performing variance analysis for MC integration. At the same time, it reveals that, when combined with correlated sampling, IS can impact convergence rate by introducing or inhibiting discontinuities in the integrand. We demonstrate that the convergence of multiple importance sampling (MIS) is determined by the strategy which converges slowest and propose several simple approaches to overcome this limitation. We show that smoothing light boundaries (as commonly done in production to reduce variance) can improve (M)IS convergence (at a cost of introducing a small amount of bias) since it removes discontinuities within the integration domain. We also propose practical integrand‐ and sample‐mirroring approaches which cancel the impact of boundary discontinuities on the convergence rate of estimators.
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    Generating Parametric BRDFs from Natural Language Descriptions
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Memery, Sean; Cedron, Osmar; Subr, Kartic; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Artistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to address different aspects of generating 3D content, such as generating meshes, arranging geometry, synthesizing textures, etc. In this paper we develop a model to generate Bidirectional Reflectance Distribution Functions (BRDFs) from descriptive textual prompts. BRDFs are four dimensional probability distributions that characterize the interaction of light with surface materials. They are either represented parametrically, or by tabulating the probability density associated with every pair of incident and outgoing angles. The former lends itself to artistic editing while the latter is used when measuring the appearance of real materials. Numerous works have focused on hypothesizing BRDF models from images of materials.We learn a mapping from textual descriptions of materials to parametric BRDFs. Our model is first trained using a semi-supervised approach before being tuned via an unsupervised scheme. Although our model is general, in this paper we specifically generate parameters for MDL materials, conditioned on natural language descriptions, within NVIDIA's Omniverse platform. This enables use cases such as real-time text prompts to change materials of objects in 3D environments such as ''dull plastic'' or ''shiny iron''. Since the output of our model is a parametric BRDF, rather than an image of the material, it may be used to render materials using any shape under arbitrarily specified viewing and lighting conditions.
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    Q-NET: A Network for Low-dimensional Integrals of Neural Proxies
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Subr, Kartic; Bousseau, Adrien and McGuire, Morgan
    Integrals of multidimensional functions are often estimated by averaging function values at multiple locations. The use of an approximate surrogate or proxy for the true function is useful if repeated evaluations are necessary. A proxy is even more useful if its own integral is known analytically and can be calculated practically. We design a family of fixed networks, which we call Q-NETs, that can calculate integrals of functions represented by sigmoidal universal approximators. Q-NETs operate on the parameters of the trained proxy and can calculate exact integrals over any subset of dimensions of the input domain. Q-NETs also facilitate convenient recalculation of integrals without resampling the integrand or retraining the proxy, under certain transformations to the input space. We highlight the benefits of this scheme for diverse rendering applications including inverse rendering, sampled procedural noise and visualization. Q-NETs are appealing in the following contexts: the dimensionality is low (< 10D); integrals of a sampled function need to be recalculated over different sub-domains; the estimation of integrals needs to be decoupled from the sampling strategy such as when sparse, adaptive sampling is used; marginal functions need to be known in functional form; or when powerful Single Instruction Multiple Data/Thread (SIMD/SIMT) pipelines are available.