We present the Toast++ open-source software environment for solving the forward and inverse problems in diffuse optical tomography (DOT). The software suite consists of a set of libraries to simulate ...near-infrared light propagation in highly scattering media with complex boundaries and heterogeneous internal parameter distribution, based on a finite-element solver. Steady-state, time- and frequency-domain data acquisition systems can be modeled. The forward solver is implemented in C++ and supports performance acceleration with parallelization for shared and distributed memory architectures, as well as graphics processing computation. Building on the numerical forward solver, Toast++ contains model-based iterative inverse solvers for reconstructing the volume distribution of absorption and scattering parameters from boundary measurements of light transmission. A range of regularization methods are provided, including the possibility of incorporating prior knowledge of internal structure. The user can link to the Toast++ libraries either directly to compile application programs for DOT, or make use of the included MATLAB and PYTHON bindings to generate script-based solutions. This approach allows rapid prototyping and provides a rich toolset in both environments for debugging, testing, and visualization.
Monte Carlo techniques for light transport simulation rely on importance sampling when constructing light transport paths. Previous work has shown that suitable sampling distributions can be ...recovered from particles distributed in the scene prior to rendering. We propose to represent the distributions by a parametric mixture model trained in an on-line (i.e. progressive) manner from a potentially infinite stream of particles. This enables recovering good sampling distributions in scenes with complex lighting, where the necessary number of particles may exceed available memory. Using these distributions for sampling scattering directions and light emission significantly improves the performance of state-of-the-art light transport simulation algorithms when dealing with complex lighting.
Regression-based Monte Carlo integration Salaün, Corentin; Gruson, Adrien; Hua, Binh-Son ...
ACM transactions on graphics,
07/2022, Letnik:
41, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Monte Carlo integration is typically interpreted as an estimator of the expected value using stochastic samples. There exists an alternative interpretation in calculus where Monte Carlo integration ...can be seen as estimating a constant function---from the stochastic evaluations of the integrand---that integrates to the original integral. The integral mean value theorem states that this constant function should be the mean (or expectation) of the integrand. Since both interpretations result in the same estimator, little attention has been devoted to the calculus-oriented interpretation. We show that the calculus-oriented interpretation actually implies the possibility of using a more complex function than a constant one to construct a more efficient estimator for Monte Carlo integration. We build a new estimator based on this interpretation and relate our estimator to control variates with least-squares regression on the stochastic samples of the integrand. Unlike prior work, our resulting estimator is provably better than or equal to the conventional Monte Carlo estimator. To demonstrate the strength of our approach, we introduce a practical estimator that can act as a simple drop-in replacement for conventional Monte Carlo integration. We experimentally validate our framework on various light transport integrals. The code is available at https://github.com/iribis/regressionmc.
With the wider availability of full‐color 3D printers, color‐accurate 3D‐print preparation has received increased attention. A key challenge lies in the inherent translucency of commonly used print ...materials that blurs out details of the color texture. Previous work tries to compensate for these scattering effects through strategic assignment of colored primary materials to printer voxels. To date, the highest‐quality approach uses iterative optimization that relies on computationally expensive Monte Carlo light transport simulation to predict the surface appearance from subsurface scattering within a given print material distribution; that optimization, however, takes in the order of days on a single machine. In our work, we dramatically speed up the process by replacing the light transport simulation with a data‐driven approach. Leveraging a deep neural network to predict the scattering within a highly heterogeneous medium, our method performs around two orders of magnitude faster than Monte Carlo rendering while yielding optimization results of similar quality level. The network is based on an established method from atmospheric cloud rendering, adapted to our domain and extended by a physically motivated weight sharing scheme that substantially reduces the network size. We analyze its performance in an end‐to‐end print preparation pipeline and compare quality and runtime to alternative approaches, and demonstrate its generalization to unseen geometry and material values. This for the first time enables full heterogenous material optimization for 3D‐print preparation within time frames in the order of the actual printing time.
In this manuscript, inspired by a simpler reformulation of primary sample space Metropolis light transport, we derive a novel family of general Markov chain Monte Carlo algorithms called
charted ...Metropolis-Hastings
, that introduces the notion of
sampling charts
to extend a given sampling domain and make it easier to sample the desired target distribution and escape from local maxima through coordinate changes. We further apply the novel algorithms to light transport simulation, obtaining a new type of algorithm called
charted Metropolis light transport
, that can be seen as a bridge between primary sample space and path space Metropolis light transport. The new algorithms require to provide only right inverses of the sampling functions, a property that we believe crucial to make them practical in the context of light transport simulation.
Gradient-domain path reusing Bauszat, Pablo; Petitjean, Victor; Eisemann, Elmar
ACM transactions on graphics,
12/2017, Letnik:
36, Številka:
6
Journal Article
Recenzirano
Monte-Carlo rendering algorithms have traditionally a high computational cost, because they rely on tracing up to billions of light paths through a scene to physically simulate light transport. ...Traditional path reusing amortizes the cost of path sampling over multiple pixels, but introduces visually unpleasant correlation artifacts and cannot handle scenes with specular light transport. We present
gradient-domain path reusing
, a novel unbiased Monte-Carlo rendering technique, which merges the concept of path reusing with the recently introduced idea of gradient-domain rendering. Since correlation is a key element in gradient sampling, it is a natural fit to be performed together with path reusing and we show that the typical artifacts of path reusing are significantly reduced by exploiting the gradient domain. Further, by employing the tools for shifting paths that were designed in the context of gradient-domain rendering over the last years, we can generalize path reusing to support arbitrary scenes including specular light transport. Our method is unbiased and currently the fastest converging unidirectional rendering technique outperforming conventional and gradient-domain path tracing by up to almost an order of magnitude.
Two decades have passed since the introduction of Markov chain Monte Carlo (MCMC) into light transport simulation by Veach and Guibas, and numerous follow-up works have been published since then. ...However, up until now no survey has attempted to cover the majority of these methods. The aim of this paper is therefore to offer a first comprehensive survey of MCMC algorithms for light transport simulation. The methods presented in this paper are categorized by their objectives and properties, while we point out their strengths and weaknesses. We discuss how the methods handle the main issues of MCMC and how they could be combined or improved in the near future. To make the paper suitable for readers unacquainted with MCMC methods, we include an introduction to general MCMC and its demonstration on a simple example.
Efficiently simulating light transport in various scenes with a single algorithm is a difficult and important problem in computer graphics. Two major issues have been shown to hinder the efficiency ...of the existing solutions: light transport due to multiple highly glossy or specular interactions, and scenes with complex visibility between the camera and light sources. While recent bidirectional path sampling methods such as vertex connection and merging/unified path sampling (VCM/UPS) efficiently deal with highly glossy or specular transport, they tend to perform poorly in scenes with complex visibility. On the other hand, Markov chain Monte Carlo (MCMC) methods have been able to show some excellent results in scenes with complex visibility, but they behave unpredictably in scenes with glossy or specular surfaces due to their fundamental issue of sample correlation. In this paper, we show how to fuse the underlying key ideas behind VCM/UPS and MCMC into a single, efficient light transport solution. Our algorithm is specifically designed to retain the advantages of both approaches, while alleviating their limitations. Our experiments show that the algorithm can efficiently render scenes with both highly glossy or specular materials
and
complex visibility, without compromising the performance in simpler cases.
Integral equations and machine learning Keller, Alexander; Dahm, Ken
Mathematics and computers in simulation,
July 2019, 2019-07-00, Letnik:
161
Journal Article
Recenzirano
Odprti dostop
As both light transport simulation and reinforcement learning are ruled by the same Fredholm integral equation of the second kind, reinforcement learning techniques may be used for photorealistic ...image synthesis: Efficiency may be dramatically improved by guiding light transport paths by an approximate solution of the integral equation that is learned during rendering. In the light of the recent advances in reinforcement learning for playing games, we investigate the representation of an approximate solution of an integral equation by artificial neural networks and derive a loss function for that purpose. The resulting Monte Carlo and quasi-Monte Carlo methods train neural networks with standard information instead of linear information and naturally are able to generate an arbitrary number of training samples. The methods are demonstrated for applications in light transport simulation.
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