83 citations · 93 across the 4 of their papers we have counts for
6 papers
RGBX-Next: Towards Realistic Generative Rendering from G-Buffers
Zheng Zeng, Marco Salvi, Lifan Wu +9
Diffusion models have achieved impressive results in image, video, and streaming generation. However, compared to traditional 3D rendering, they still lack precise control over the…
Inverse Global Illumination using a Neural Radiometric Prior
Saeed Hadadan, Geng Lin, Jan Novák +2
Inverse rendering methods that account for global illumination are becoming more popular, but current methods require evaluating and automatically differentiating millions of path…
Neural Control Variates
Thomas Müller, Fabrice Rousselle, Jan Novák +1
We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration. So far, the core challenge of applying the method of control variate…
Neural Importance Sampling
Thomas Müller, Brian McWilliams, Fabrice Rousselle +2
We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend…
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
Simon Kallweit, Thomas Müller, Brian McWilliams +2
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie s…
Reversible Jump Metropolis Light Transport using Inverse Mappings
Benedikt Bitterli, Wenzel Jakob, Jan Novák +1
We study Markov Chain Monte Carlo (MCMC) methods operating in primary sample space and their interactions with multiple sampling techniques. We observe that incorporating the sampl…