no code implementations • 3 May 2023 • Saeed Hadadan, Geng Lin, Jan Novák, Fabrice Rousselle, Matthias Zwicker
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).
2 code implementations • 23 Jun 2021 • Thomas Müller, Fabrice Rousselle, Jan Novák, Alexander Keller
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.
no code implementations • 2 Jun 2020 • Thomas Müller, Fabrice Rousselle, Jan Novák, Alexander Keller
We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration.
2 code implementations • 11 Aug 2018 • Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák
We propose to use deep neural networks for generating samples in Monte Carlo integration.
no code implementations • 15 Sep 2017 • Simon Kallweit, Thomas Müller, Brian McWilliams, Markus Gross, Jan Novák
To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source.
no code implementations • ACM Transactions on Graphics 2017 • Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony Derose, Fabrice Rousselle
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.