Rendering 2024 - Symposium Track

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https://diglib.eg.org/handle/10.2312/3607012

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Browsing Rendering 2024 - Symposium Track by Subject "CCS Concepts: Computing methodologies -> Ray tracing"

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    Computing Manifold Next-Event Estimation without Derivatives using the Nelder-Mead Method
    (The Eurographics Association, 2024) Granizo-Hidalgo, Ana; Holzschuch, Nicolas; Haines, Eric; Garces, Elena
    Specular surfaces, by focusing the light that is being reflected or refracted, cause bright spots in the scene, called caustics. These caustics are challenging to compute for global illumination algorithms. Manifold-based methods (Manifold Exploration, Manifold Next-Event Estimation, Specular Next Event Estimation) compute these caustics as the zeros of an objective function, using the Newton-Raphson method. They are efficient, but require computing the derivatives of the objective function, which in turn requires local surface derivatives around the reflection point, which can be challenging to implement. In this paper, we leverage the Nelder-Mead method to compute caustics using Manifold Next-Event Estimation without having to compute local derivatives. Our method only requires local evaluations of the objective function, making it an easy addition to any path-tracing algorithm.
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    Path Sampling Methods for Differentiable Rendering
    (The Eurographics Association, 2024) Su, Tanli; Gkioulekas, Ioannis; Haines, Eric; Garces, Elena
    We introduce a suite of path sampling methods for differentiable rendering of scene parameters that do not induce visibility-driven discontinuities, such as BRDF parameters. We begin by deriving a path integral formulation for differentiable rendering of such parameters, which we then use to derive methods that importance sample paths according to this formulation. Our methods are analogous to path tracing and path tracing with next event estimation for primal rendering, have linear complexity, and can be implemented efficiently using path replay backpropagation. Our methods readily benefit from differential BRDF sampling routines, and can be further enhanced using multiple importance sampling and a loss-aware pixel-space adaptive sampling procedure tailored to our path integral formulation. We show experimentally that our methods reduce variance in rendered gradients by potentially orders of magnitude, and thus help accelerate inverse rendering optimization of BRDF parameters.