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Learning to Importance Sample in Primary Sample Space
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/c2013-0-15557-2openalex-snapshot
Physically Based Rendering: From Theory to Implementation
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
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Image quality assessment: from error visibility to structural similarity
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A Simple and Robust Mutation Strategy for the Metropolis Light Transport Algorithm
inferredcites →https://doi.org/10.1111/cgf.12417openalex-snapshot
Importance Sampling Microfacet‐Based BSDFs using the Distribution of Visible Normals
inferredcites →https://doi.org/10.1111/cgf.13227openalex-snapshot
Practical Path Guiding for Efficient Light‐Transport Simulation
inferredcites →https://doi.org/10.1145/218380.218498openalex-snapshot
Optimally combining sampling techniques for Monte Carlo rendering
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Global illumination with radiance regression functions
inferredcites →https://doi.org/10.1145/2601097.2601108openalex-snapshot
The natural-constraint representation of the path space for efficient light transport simulation
inferredcites →https://doi.org/10.1145/2601097.2601138openalex-snapshot
Multiplexed metropolis light transport
inferredcites →https://doi.org/10.1145/2601097.2601203openalex-snapshot
On-line learning of parametric mixture models for light transport simulation
inferredcites →https://doi.org/10.1145/2816795.2818084openalex-snapshot
Anisotropic Gaussian mutations for metropolis light transport through Hessian-Hamiltonian dynamics
inferredcites →https://doi.org/10.1145/3072959.3073601openalex-snapshot
Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder
inferredcites →https://doi.org/10.1145/3072959.3073691openalex-snapshot
Fusing state spaces for markov chain Monte Carlo rendering
inferredcites →https://doi.org/10.1145/3072959.3073708openalex-snapshot
Kernel-predicting convolutional networks for denoising Monte Carlo renderings
inferredcites →https://doi.org/10.1145/3084363.3085032openalex-snapshot
Learning light transport the reinforced way
inferredcites →https://doi.org/10.1145/3132704openalex-snapshot
Reversible Jump Metropolis Light Transport Using Inverse Mappings
inferredcites →https://doi.org/10.1145/3197517.3201340openalex-snapshot
Bayesian online regression for adaptive direct illumination sampling
inferredcites →https://doi.org/10.48550/arxiv.1709.05418openalex-snapshot
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Enhanced Direct Lighting Using Visibility-Aware Light Sampling
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Artificial intelligence for virtual reality: a review
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A survey on rendering homogeneous participating media
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Neural Global Illumination: Interactive Indirect Illumination Prediction Under Dynamic Area Lights
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Deep‐learning the Latent Space of Light Transport
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Practical Product Path Guiding Using Linearly Transformed Cosines
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Can't Invert the CDF? The Triangle‐Cut Parameterization of the Region under the Curve
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Practical Product Sampling by Fitting and Composing Warps
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Learning Multiple‐Scattering Solutions for Sphere‐Tracing of Volumetric Subsurface Effects
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Neural Acceleration of Scattering‐Aware Color 3D Printing
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Neural BRDF Representation and Importance Sampling
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Dynamic Diffuse Global Illumination Resampling
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Path Guiding Using Spatio‐Directional Mixture Models
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Path Guiding with Vertex Triplet Distributions
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Real‐time Deep Radiance Reconstruction from Imperfect Caches
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Markov Chain Mixture Models for Real‐Time Direct Illumination
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Volume Path Guiding Based on Zero-Variance Random Walk Theory
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Compositional neural scene representations for shading inference
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Weakly-supervised contrastive learning in path manifold for Monte Carlo image reconstruction
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Primary-space Adaptive Control Variates Using Piecewise-polynomial Approximations
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Light Source Selection in Primary-Sample-Space Neural Photon Sampling
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Efficient estimation of boundary integrals for path-space differentiable rendering
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Self-Supervised Post-Correction for Monte Carlo Denoising
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Conditional Mixture Path Guiding for Differentiable Rendering
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Neural Product Importance Sampling via Warp Composition
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BSDF importance sampling using a diffusion model
inferred← citeshttps://doi.org/10.1145/3680528.3687685openalex-snapshot
Dynamic Neural Radiosity with Multi-grid Decomposition
inferred← citeshttps://doi.org/10.1145/3680528.3687687openalex-snapshot
Efficient Neural Path Guiding with 4D Modeling
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Real-Time Markov Chain Path Guiding for Global Illumination and Single Scattering
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Exploring phase space with Neural Importance Sampling
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Enhancement automatic speech recognition by deep neural networks
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Applying Neural Importance Sampling to gluon scattering
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An Adaptive Shooting and Bouncing Ray Method Based on Q-Learning for Efficient Synthetic Aperture Radar Imaging Simulation
inferred← citeshttps://doi.org/10.48550/arxiv.2005.12518openalex-snapshot
Survey: Machine Learning in Production Rendering
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Improving Monte Carlo simulations in high energy physics using machine learning techniques
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Learning radiance fields : from global illumination to generative models
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