Rendering 2021 - DL-only Track

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https://diglib.eg.org/handle/10.2312/2633075

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Browsing Rendering 2021 - DL-only Track by Subject "Computing methodologies --> Rendering"

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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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    Stochastic Generation of (t, s) Sample Sequences
    (The Eurographics Association, 2021) Helmer, Andrew; Christensen, Per; Kensler, Andrew; Bousseau, Adrien and McGuire, Morgan
    We introduce a novel method to generate sample sequences that are progressively stratified both in high dimensions and in lower-dimensional projections. Our method comes from a new observation that Owen-scrambled quasi-Monte Carlo (QMC) sequences can be generated as stratified samples, merging the QMC construction and random scrambling into a stochastic algorithm. This yields simpler implementations of Owen-scrambled Sobol', Halton, and Faure sequences that exceed the previous state-of-the-art sample-generation speed; we provide an implementation of Owen-scrambled Sobol' (0,2)-sequences in fewer than 30 lines of C++ code that generates 200 million samples per second on a single CPU thread. Inspired by pmj02bn sequences, this stochastic formulation allows multidimensional sequences to be augmented with best-candidate sampling to improve point spacing in arbitrary projections. We discuss the applications of these high-dimensional sequences to rendering, describe a new method to decorrelate sequences while maintaining their progressive properties, and show that an arbitrary sample coordinate can be queried efficiently. Finally we show how the simplicity and local differentiability of our method allows for further optimization of these sequences. As an example, we improve progressive distances of scrambled Sobol' (0,2)-sequences using a (sub)gradient descent optimizer, which generates sequences with near-optimal distances.