Browsing by Author "Hua, Binh-Son"

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    Neural Sequence Transformation
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Mukherjee, Sabyasachi; Mukherjee, Sayan; Hua, Binh-Son; Umetani, Nobuyuki; Meister, Daniel; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
    Monte Carlo integration is a technique for numerically estimating a definite integral by stochastically sampling its integrand. These samples can be averaged to make an improved estimate, and the progressive estimates form a sequence that converges to the integral value on the limit. Unfortunately, the sequence of Monte Carlo estimates converges at a rate of O(pn), where n denotes the sample count, effectively slowing down as more samples are drawn. To overcome this, we can apply sequence transformation, which transforms one converging sequence into another with the goal of accelerating the rate of convergence. However, analytically finding such a transformation for Monte Carlo estimates can be challenging, due to both the stochastic nature of the sequence, and the complexity of the integrand. In this paper, we propose to leverage neural networks to learn sequence transformations that improve the convergence of the progressive estimates of Monte Carlo integration. We demonstrate the effectiveness of our method on several canonical 1D integration problems as well as applications in light transport simulation.
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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.