5 citations · 5 across the 4 of their papers we have counts for
6 papers
VENI: Variational Encoder for Natural Illumination
Paul Walker, James A. D. Gardner, Andreea Ardelean +2
Inverse rendering is an ill-posed problem, but priors such as illumination priors can help simplify it. Existing work either disregards the spherical and rotation-equivariant natur…
TAPVid-360: Tracking Any Point in 360 from Narrow Field of View Video
Finlay G. C. Hudson, James A. D. Gardner, William A. P. Smith
Humans excel at constructing panoramic mental models of their surroundings, maintaining object permanence and inferring scene structure beyond visible regions. In contrast, current…
NeuRaLaTeX: A machine learning library written in pure LaTeX
James A. D. Gardner, Will Rowan, William A. P. Smith
In this paper, we introduce NeuRaLaTeX, which we believe to be the first deep learning library written entirely in LaTeX. As part of your LaTeX document you can specify the archite…
RENI++ A Rotation-Equivariant, Scale-Invariant, Natural Illumination Prior
James A. D. Gardner, Bernhard Egger, William A. P. Smith
Inverse rendering is an ill-posed problem. Previous work has sought to resolve this by focussing on priors for object or scene shape or appearance. In this work, we instead focus o…
The Sky's the Limit: Re-lightable Outdoor Scenes via a Sky-pixel Constrained Illumination Prior and Outside-In Visibility
James A. D. Gardner, Evgenii Kashin, Bernhard Egger +1
Inverse rendering of outdoor scenes from unconstrained image collections is a challenging task, particularly illumination/albedo ambiguities and occlusion of the illumination envir…
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior
James A. D. Gardner, Bernhard Egger, William A. P. Smith
Inverse rendering is an ill-posed problem. Previous work has sought to resolve this by focussing on priors for object or scene shape or appearance. In this work, we instead focus o…