11 citations · 13 across the 5 of their papers we have counts for
7 papers
Stochastic Ray Tracing of Transparent 3D Gaussians
Xin Sun, Iliyan Georgiev, Yun Fei +1
3D Gaussian splatting has been widely adopted as a 3D representation for novel-view synthesis, relighting, and 3D generation tasks. It delivers realistic and detailed results throu…
Canonical correlation analysis of stochastic trends via functional approximation
Massimo Franchi, Iliyan Georgiev, Paolo Paruolo
This paper proposes a novel approach for semiparametric inference on the number of common trends and their loading matrix in systems. It combines functional app…
SAMa: Material-aware 3D Selection and Segmentation
Michael Fischer, Iliyan Georgiev, Thibault Groueix +3
Decomposing 3D assets into material parts is a common task for artists, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selec…
Multiple Importance Sampling for Stochastic Gradient Estimation
Corentin Salaün, Xingchang Huang, Iliyan Georgiev +2
We introduce a theoretical and practical framework for efficient importance sampling of mini-batch samples for gradient estimation from single and multiple probability distribution…
N-BVH: Neural ray queries with bounding volume hierarchies
Philippe Weier, Alexander Rath, Élie Michel +3
Neural representations have shown spectacular ability to compress complex signals in a fraction of the raw data size. In 3D computer graphics, the bulk of a scene's memory usage is…
Online Importance Sampling for Stochastic Gradient Optimization
Corentin Salaün, Xingchang Huang, Iliyan Georgiev +2
Machine learning optimization often depends on stochastic gradient descent, where the precision of gradient estimation is vital for model performance. Gradients are calculated from…
Perceptual error optimization for Monte Carlo animation rendering
Miša Korać, Corentin Salaün, Iliyan Georgiev +4
Independently estimating pixel values in Monte Carlo rendering results in a perceptually sub-optimal white-noise distribution of error in image space. Recent works have shown that…