Ensemble Metropolis Light Transport

Bashford-Rogers, Thomas; Santos, Luís Paulo; Marnerides, Demetris; Debattista, Kurt

doi:10.1145/3472294

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…Bidirectional path-tracing constructs paths from both the camera and the light sources and attempts to connect the different path nodes, to increase the quality of light energy capture [28]. Metropolis light transport constructs paths from the eye to a light source using bidirectional path tracing and then applies minor changes to the paths to explore the neighborhood of those paths [29]. Path-leading techniques aim to more intelligent find the areas that appear most interesting for illumination [30].…”

Section: Global Illumination Methodologymentioning

confidence: 99%

Heterogeneous Regularization for Fast Rendering using Deep Spike Neural Network

Constantin

Dornaika

et al. 2023

Preprint

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In real-time rendering, images are computed at a very high speed to make them look like the real scenes. Speed and interactivity play an important role in real-time rendering. However, computing indirect lighting in real time is a major challenge because it is very computationally intensive. To be used in video movies, the rendering program must run very fast and the video engine must operate at a constant frame rate. The images must be noise-free and the animations must remain stable without temporal artifacts. Global lighting in videos is still a real challenge. The rendering program must calculate the entire light exchange between the surfaces of a virtual 3D scene. Offline techniques are capable of rendering photo-realistic images, but none of these algorithms is capable of generating images in real time with a high number of frames per second. In this work, a Deep Spiking Neural Network with Heterogeneous Regularization learning technique is proposed with the aim of building a more biologically plausible network to evaluate the amount of noise and find a stopping criterion for fast realistic illumination. The idea is to improve learning performance by extracting the most relevant features with a deep spiking neural network to achieve high rendering quality. We used a new objective function composed from a supervised term and an unsupervised term. The supervised term enforces the fitting term between the predicted labels and the known labels. The unsupervised term imposes the smoothness of the predicted labels. The experiments was performed on scenes with global illumination containing different image distortions. The proposed model is also compared with the human visual system and other state-of-the-art models. Results show better performance and advantages in terms of efficiency, an increasingly biologically plausible network, and ease of implementation in Neuromorphic hardware.

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Section: Global Illumination Methodologymentioning

confidence: 99%

Heterogeneous Regularization for Fast Rendering using Deep Spike Neural Network

Constantin

Dornaika

et al. 2023

Preprint

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“…There exist other relevant extensions to MLT that share samples in image space through replica exchange [GWH20] or ensembles of light transport paths to guide future transition kernels [BSMD21]. Nevertheless, these works still rely on detailed balance conditions in the same fashion as classic MLT.…”

Section: Previous Workmentioning

confidence: 99%

Markov Chain Mixture Models for Real‐Time Direct Illumination

Dittebrandt,

Schüßler,

Hanika

et al. 2023

Computer Graphics Forum

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We present a novel technique to efficiently render complex direct illumination in real‐time. It is based on a spatio‐temporal randomized mixture model of von Mises‐Fisher (vMF) distributions in screen space. For every pixel we determine the vMF distribution to sample from using a Markov chain process which is targeted to capture important features of the integrand. By this we avoid the storage overhead of finite‐component deterministic mixture models, for which, in addition, determining the optimal component count is challenging. We use stochastic multiple importance sampling (SMIS) to be independent of the equilibrium distribution of our Markov chain process, since it cancels out in the estimator. Further, we use the same sample to advance the Markov chain and to construct the SMIS estimator and local Markov chain state permutations avoid the resulting bias due to dependent sampling. As a consequence we require one ray per sample and pixel only. We evaluate our technique using implementations in a research renderer as well as a classic game engine with highly dynamic content. Our results show that it is efficient and quickly readapts to dynamic conditions. We compare to spatio‐temporal resampling (ReSTIR), which can suffer from correlation artifacts due to its non‐adapting candidate distributions that can deviate strongly from the integrand. While we focus on direct illumination, our approach is more widely applicable and we exemplarily show the rendering of caustics.

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“…We augment spatiotemporal reservoir resampling in ReSTIR with sample mutations, and demonstrate the complementary strengths of resampling and mutations in this framework. In graphics, our approach is most closely related to Metropolis Light Transport (MLT) [Veach and Guibas 1997] and associated techniques [Kelemen et al 2002;Jakob and Marschner 2012;Lehtinen et al 2013;Hachisuka et al 2014;Otsu et al 2018;Lai et al , 2009Bashford-Rogers et al 2021]. In the broader Monte Carlo landscape, our approach belongs to the class of algorithms that jointly use resampling and mutations for sampling problems, such as Sequential Monte Carlo (SMC) [Doucet et al 2001] and Population Monte Carlo (PMC) [Cappé et al 2004].…”

Section: Related Workmentioning

confidence: 99%