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A Survey on Gradient-Domain Rendering

COMPUTER GRAPHICS FORUM 2019年第2期38卷 455-472页

作者： Hua, Binh-Son Gruson, Adrien Petitjean, Victor Zwicker, Matthias Nowrouzezahrai, Derek Eisemann, Elmar Hachisuka, Toshiya Univ Tokyo Tokyo Japan Delft Univ Technol Delft Netherlands Univ Maryland College Pk MD 20742 USA McGill Univ Montreal PQ Canada

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.

关键词： CCS Concepts •computing methodologies → ray tracing

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A new microflake model with microscopic self-shadowing for accurate volume downsampling

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COMPUTER GRAPHICS FORUM 2018年第2期37卷 111-121页

作者： Loubet, Guillaume Neyret, Fabrice Univ Grenoble Alpes LJK INRIA CNRS Grenoble France

Naive linear methods for downsampling high-resolution microflake volumes often produce inaccurate appearance, especially when input voxels are very opaque. Preserving correct appearance at all resolutions requires taking into account maskingshadowing effects that occur between and inside dense input voxels. We introduce a new microflake model whose additional parameters characterize self-shadowing effects at a microscopic scale. We provide an anisotropic self-shadowing function and microflake distributions for which the scattering coefficients and the phase functions of our model have closed-form expressions. We use this model in a new downsampling approach in which scattering parameters are computed from local estimations of self-shadowing probabilities in the input volume. Unlike previous work, our method handles datasets with spatially varying scattering parameters, semi-transparent volumes and datasets with intricate silhouettes. We show that our method generates LoDs with correct transparency and consistent appearance through scales for a wide range of challenging datasets, allowing for huge memory savings and efficient distant rendering without loss of quality.

关键词： CCS Concepts •computing methodologies → ray tracing Volumetric models

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Deep Adaptive Sampling for Low Sample Count Rendering

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COMPUTER GRAPHICS FORUM 2018年第4期37卷 35-44页

作者： Kuznetsov, Alexandr Kalantari, Nima Khademi Ramamoorthi, Ravi Univ Calif San Diego San Diego CA 92103 USA

Recently, deep learning approaches have proven successful at removing noise from Monte Carlo ( MC) rendered images at extremely low sampling rates, e.g., 1-4 samples per pixel (spp). While these methods provide dramatic speedups, they operate on uniformly sampled MC rendered images. However, the full promise of low sample counts requires both adaptive sampling and reconstruction/denoising. Unfortunately, the traditional adaptive sampling techniques fail to handle the cases with low sampling rates, since there is insufficient information to reliably calculate their required features, such as variance and contrast. In this paper, we address this issue by proposing a deep learning approach for joint adaptive sampling and reconstruction of MC rendered images with extremely low sample counts. Our system consists of two convolutional neural networks (CNN), responsible for estimating the sampling map and denoising, separated by a renderer. Specifically, we first render a scene with one spp and then use the first CNN to estimate a sampling map, which is used to distribute three additional samples per pixel on average adaptively. We then filter the resulting render with the second CNN to produce the final denoised image. We train both networks by minimizing the error between the denoised and ground truth images on a set of training scenes. To use backpropagation for training both networks, we propose an approach to effectively compute the gradient of the renderer. We demonstrate that our approach produces better results compared to other sampling techniques. On average, our 4 spp renders are comparable to 6 spp from uniform sampling with deep learning-based denoising. Therefore, 50% more uniformly distributed samples are required to achieve equal quality without adaptive sampling.

关键词： CCS Concepts •computing methodologies → ray tracing Neural networks Image processing

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Spectral Gradient Sampling for Path tracing

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COMPUTER GRAPHICS FORUM 2018年第4期37卷 45-53页

作者： Petitjean, Victor Bauszat, Pablo Eisemann, Elmar Delft Univ Technol Delft Netherlands

Spectral Monte-Carlo methods are currently the most powerful techniques for simulating light transport with wavelength-dependent phenomena (e.g., dispersion, colored particle scattering, or diffraction gratings). Compared to trichromatic rendering, sampling the spectral domain requires significantly more samples for noise-free images. Inspired by gradient-domain rendering, which estimates image gradients, we propose spectral gradient sampling to estimate the gradients of the spectral distribution inside a pixel. These gradients can be sampled with a significantly lower variance by carefully correlating the path samples of a pixel in the spectral domain, and we introduce a mapping function that shifts paths with wavelength-dependent interactions. We compute the result of each pixel by integrating the estimated gradients over the spectral domain using a one-dimensional screened Poisson reconstruction. Our method improves convergence and reduces chromatic noise from spectral sampling, as demonstrated by our implementation within a conventional path tracer.

关键词： CCS Concepts •computing methodologies → ray tracing

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Efficient Caustic Rendering with Lightweight Photon Mapping

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COMPUTER GRAPHICS FORUM 2018年第4期37卷 133-142页

作者： Grittmann, Pascal Perard-Gayot, Arsene Slusallek, Philipp Krivanek, Jaroslav Saarland Univ Saarbrucken Germany DFKI Saarbrucken Saarbrucken Germany Charles Univ Prague Prague Czech Republic Render Leg Prague Czech Republic

Robust and efficient rendering of complex lighting effects, such as caustics, remains a challenging task. While algorithms like vertex connection and merging can render such effects robustly, their significant overhead over a simple path tracer is not always justified and - as we show in this paper - also not necessary. In current rendering solutions, caustics often require the user to enable a specialized algorithm, usually a photon mapper, and hand-tune its parameters. But even with carefully chosen parameters, photon mapping may still trace many photons that the path tracer could sample well enough, or, even worse, that are not visible at all. Our goal is robust, yet lightweight, caustics rendering. To that end, we propose a technique to identify and focus computation on the photon paths that offer significant variance reduction over samples from a path tracer. We apply this technique in a rendering solution combining path tracing and photon mapping. The photon emission is automatically guided towards regions where the photons are useful, i.e., provide substantial variance reduction for the currently rendered image. Our method achieves better photon densities with fewer light paths (and thus photons) than emission guiding approaches based on visual importance. In addition, we automatically determine an appropriate number of photons for a given scene, and the algorithm gracefully degenerates to pure path tracing for scenes that do not benefit from photon mapping.

关键词： CCS Concepts •computing methodologies → ray tracing

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Fast Hardware Construction and Refitting of Quantized Bounding Volume Hierarchies

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COMPUTER GRAPHICS FORUM 2017年第4期36卷 167-178页

作者： Viitanen, T. Koskela, M. Jaaskelainen, P. Immonen, K. Takala, J. Tampere Univ Technol Tampere Finland

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.

关键词： CCS Concepts •computing methodologies → ray tracing Graphics processors

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Bayesian Collaborative Denoising for Monte Carlo Rendering

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COMPUTER GRAPHICS FORUM 2017年第4期36卷 137-153页

作者： Boughida, Malik Boubekeur, Tamy Paris Saclay Univ LTCI Telecom ParisTech Paris France

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.

关键词： CCS Concepts •computing methodologies → ray tracing Image processing

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Variance and Convergence Analysis of Monte Carlo Line and Segment Sampling

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COMPUTER GRAPHICS FORUM 2017年第4期36卷 79-89页

作者： Singh, Gurprit Miller, Bailey Jarosz, Wojciech Dartmouth Coll Hanover NH 03755 USA

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 C-0 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.

关键词： CCS Concepts •computing methodologies → ray tracing •Mathematics of computing → Stochastic processes Computation of transforms

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