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Found 5 papers, 2 papers with code
Inverse Global Illumination using a Neural Radiometric Prior
We train our radiance network and optimize scene parameters simultaneously using a loss consisting of both a photometric term between renderings and the multi-view input images, and our radiometric prior (the residual term).
Real-time Neural Radiance Caching for Path Tracing
Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i. e. we opt for training the radiance cache while rendering.
Neural Control Variates
We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration.
Neural Importance Sampling
We propose to use deep neural networks for generating samples in Monte Carlo integration.
Kernel-predicting convolutional networks for denoising monte carlo renderings.
In a second approach, we introduce a novel, kernel-prediction network which uses the CNN to estimate the local weighting kernels used to compute each denoised pixel from its neighbors.
