Search Results for author:
Found 6 papers, 0 papers with code
Blue noise for diffusion models
In this paper, we introduce a novel and general class of diffusion models taking correlated noise within and across images into account.
Efficient Gradient Estimation via Adaptive Sampling and Importance Sampling
Nevertheless, existing algorithms have struggled to efficiently integrate importance sampling into machine learning frameworks.
Joint Sampling and Optimisation for Inverse Rendering
By combining proportional and finite-difference samples, we continuously reduce the variance of our novel gradient meta-estimators throughout the optimisation process.
Neural Relightable Participating Media Rendering
We propose to learn neural representations for participating media with a complete simulation of global illumination.
Perceptual error optimization for Monte Carlo rendering
To find solutions, we present a set of algorithms that provide varying trade-offs between quality and speed, showing substantial improvements over prior state of the art.
Image Generation
Graphics
I.3.7
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry
Deep implicit field regression methods are effective for 3D reconstruction from single-view images.
