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A survey on deep learning-based Monte Carlo denoising
Monte Carlo (MC) integration is used ubiquitously in realistic image synthesis because of its
flexibility and generality. However, the integration has to balance estimator bias and …
flexibility and generality. However, the integration has to balance estimator bias and …
Shape, light, and material decomposition from images using monte carlo rendering and denoising
Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D
scenes from multi-view images. Most methods rely on simple rendering algorithms: pre …
scenes from multi-view images. Most methods rely on simple rendering algorithms: pre …
Real-time neural radiance caching for path tracing
We present a real-time neural radiance caching method for path-traced global illumination.
Our system is designed to handle fully dynamic scenes, and makes no assumptions about …
Our system is designed to handle fully dynamic scenes, and makes no assumptions about …
Nefii: Inverse rendering for reflectance decomposition with near-field indirect illumination
Inverse rendering methods aim to estimate geometry, materials and illumination from multi-
view RGB images. In order to achieve better decomposition, recent approaches attempt to …
view RGB images. In order to achieve better decomposition, recent approaches attempt to …
All-optical image denoising using a diffractive visual processor
Image denoising, one of the essential inverse problems, targets to remove noise/artifacts
from input images. In general, digital image denoising algorithms, executed on computers …
from input images. In general, digital image denoising algorithms, executed on computers …
Generalized resampled importance sampling: Foundations of restir
As scenes become ever more complex and real-time applications embrace ray tracing, path
sampling algorithms that maximize quality at low sample counts become vital. Recent …
sampling algorithms that maximize quality at low sample counts become vital. Recent …
Recursive control variates for inverse rendering
We present a method for reducing errors---variance and bias---in physically based
differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as …
differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as …
Vertex block descent
We introduce vertex block descent, a block coordinate descent solution for the variational
form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local …
form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local …
Random-access neural compression of material textures
The continuous advancement of photorealism in rendering is accompanied by a growth in
texture data and, consequently, increasing storage and memory demands. To address this …
texture data and, consequently, increasing storage and memory demands. To address this …
Interactive Monte Carlo denoising using affinity of neural features
High-quality denoising of Monte Carlo low-sample renderings remains a critical challenge
for practical interactive ray tracing. We present a new learning-based denoiser that achieves …
for practical interactive ray tracing. We present a new learning-based denoiser that achieves …