Volume 36 (2017)

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Browsing Volume 36 (2017) by Subject "> Ray tracing"

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    Bayesian Collaborative Denoising for Monte Carlo Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2017) Boughida, Malik; Boubekeur, Tamy; Zwicker, Matthias and Sander, Pedro
    The stochastic nature of Monte Carlo rendering algorithms inherently produces noisy images. Essentially, three approaches have been developed to solve this issue: improving the ray-tracing strategies to reduce pixel variance, providing adaptive sampling by increasing the number of rays in regions needing so, and filtering the noisy image as a post-process. Although the algorithms from the latter category introduce bias, they remain highly attractive as they quickly improve the visual quality of the images, are compatible with all sorts of rendering effects, have a low computational cost and, for some of them, avoid deep modifications of the rendering engine. In this paper, we build upon recent advances in both non-local and collaborative filtering methods to propose a new efficient denoising operator for Monte Carlo rendering. Starting from the local statistics which emanate from the pixels sample distribution, we enrich the image with local covariance measures and introduce a nonlocal bayesian filter which is specifically designed to address the noise stemming from Monte Carlo rendering. The resulting algorithm only requires the rendering engine to provide for each pixel a histogram and a covariance matrix of its color samples. Compared to state-of-the-art sample-based methods, we obtain improved denoising results, especially in dark areas, with a large increase in speed and more robustness with respect to the main parameter of the algorithm. We provide a detailed mathematical exposition of our bayesian approach, discuss extensions to multiscale execution, adaptive sampling and animated scenes, and experimentally validate it on a collection of scenes.
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    Fast Hardware Construction and Refitting of Quantized Bounding Volume Hierarchies
    (The Eurographics Association and John Wiley & Sons Ltd., 2017) Viitanen, Timo; Koskela, Matias; Jääskeläinen, Pekka; Immonen, Kalle; Takala, Jarmo; Zwicker, Matthias and Sander, Pedro
    There is recent interest in GPU architectures designed to accelerate ray tracing, especially on mobile systems with limited memory bandwidth. A promising recent approach is to store and traverse Bounding Volume Hierarchies (BVHs), used to accelerate ray tracing, in low arithmetic precision. However, so far there is no research on refitting or construction of such compressed BVHs, which is necessary for any scenes with dynamic content. We find that in a hardware-accelerated tree update, significant memory traffic and runtime savings are available from streaming, bottom-up compression. Novel algorithmic techniques of modulo encoding and treelet-based compression are proposed to reduce backtracking inherent in bottom-up compression. Together, these techniques reduce backtracking to a small fraction. Compared to a separate top-down compression pass, streaming bottom-up compression with the proposed optimizations saves on average 42% of memory accesses for LBVH construction and 56% for refitting of compressed BVHs, over 16 test scenes. In architectural simulation, the proposed streaming compression reduces LBVH runtime by 20% compared to a single-precision build, and 41% compared to a single-precision build followed by top-down compression. Since memory traffic dominates the energy cost of refitting and LBVH construction, energy consumption is expected to fall by a similar fraction.
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    Line Integration for Rendering Heterogeneous Emissive Volumes
    (The Eurographics Association and John Wiley & Sons Ltd., 2017) Simon, Florian; Hanika, Johannes; Zirr, Tobias; Dachsbacher, Carsten; Zwicker, Matthias and Sander, Pedro
    Emissive media are often challenging to render: in thin regions where only few scattering events occur the emission is poorly sampled, while sampling events for emission can be disadvantageous due to absorption in dense regions. We extend the standard path space measurement contribution to also collect emission along path segments, not only at vertices. We apply this extension to two estimators: extending paths via scattering and distance sampling, and next event estimation. In order to do so, we unify the two approaches and derive the corresponding Monte Carlo estimators to interpret next event estimation as a solid angle sampling technique. We avoid connecting paths to vertices hidden behind dense absorbing layers of smoke by also including transmittance sampling into next event estimation. We demonstrate the advantages of our line integration approach which generates estimators with lower variance since entire segments are accounted for. Also, our novel forward next event estimation technique yields faster run times compared to previous next event estimation as it penetrates less deeply into dense volumes.
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    Variance and Convergence Analysis of Monte Carlo Line and Segment Sampling
    (The Eurographics Association and John Wiley & Sons Ltd., 2017) Singh, Gurprit; Miller, Bailey; Jarosz, Wojciech; Zwicker, Matthias and Sander, Pedro
    Recently researchers have started employing Monte Carlo-like line sample estimators in rendering, demonstrating dramatic reductions in variance (visible noise) for effects such as soft shadows, defocus blur, and participating media. Unfortunately, there is currently no formal theoretical framework to predict and analyze Monte Carlo variance using line and segment samples which have inherently anisotropic Fourier power spectra. In this work, we propose a theoretical formulation for lines and finite-length segment samples in the frequency domain that allows analyzing their anisotropic power spectra using previous isotropic variance and convergence tools. Our analysis shows that judiciously oriented line samples not only reduce the dimensionality but also pre-filter C0 discontinuities, resulting in further improvement in variance and convergence rates. Our theoretical insights also explain how finite-length segment samples impact variance and convergence rates only by pre-filtering discontinuities. We further extend our analysis to consider (uncorrelated) multi-directional line (segment) sampling, showing that such schemes can increase variance compared to unidirectional sampling. We validate our theoretical results with a set of experiments including direct lighting, ambient occlusion, and volumetric caustics using points, lines, and segment samples.