Fabrice Rousselle
Fabrice Rousselle
Senior Research Scientist, NVIDIA Research
Verified email at nvidia.com - Homepage
Cited by
Cited by
Kernel-predicting convolutional networks for denoising Monte Carlo renderings.
S Bako, T Vogels, B McWilliams, M Meyer, J Novák, A Harvill, P Sen, ...
ACM Trans. Graph. 36 (4), 97:1-97:14, 2017
Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering
M Zwicker, W Jarosz, J Lehtinen, B Moon, R Ramamoorthi, F Rousselle, ...
Computer graphics forum 34 (2), 667-681, 2015
Adaptive rendering with non-local means filtering
F Rousselle, C Knaus, M Zwicker
ACM Transactions on Graphics (TOG) 31 (6), 1-11, 2012
Adaptive sampling and reconstruction using greedy error minimization
F Rousselle, C Knaus, M Zwicker
ACM Transactions on Graphics (TOG) 30 (6), 1-12, 2011
Robust denoising using feature and color information
F Rousselle, M Manzi, M Zwicker
Computer Graphics Forum 32 (7), 121-130, 2013
Neural importance sampling
T Müller, B McWilliams, F Rousselle, M Gross, J Novák
ACM Transactions on Graphics (TOG) 38 (5), 1-19, 2019
Nonlinearly weighted first‐order regression for denoising Monte Carlo renderings
B Bitterli, F Rousselle, B Moon, JA Iglesias‐Guitián, D Adler, K Mitchell, ...
Computer Graphics Forum 35 (4), 107-117, 2016
Denoising with kernel prediction and asymmetric loss functions
T Vogels, F Rousselle, B McWilliams, G Röthlin, A Harvill, D Adler, ...
ACM Transactions on Graphics (TOG) 37 (4), 1-15, 2018
Recent advances in facial appearance capture
O Klehm, F Rousselle, M Papas, D Bradley, C Hery, B Bickel, W Jarosz, ...
Computer Graphics Forum 34 (2), 709-733, 2015
Path‐space motion estimation and decomposition for robust animation filtering
H Zimmer, F Rousselle, W Jakob, O Wang, D Adler, W Jarosz, ...
Computer Graphics Forum 34 (4), 131-142, 2015
Image-space control variates for rendering
F Rousselle, W Jarosz, J Novák
ACM Transactions on Graphics (TOG) 35 (6), 1-12, 2016
Denoising Monte Carlo renderings using machine learning with importance sampling
T Vogels, F Rousselle, B Mcwilliams, M Meyer, J Novak
US Patent 10,572,979, 2020
Efficient product sampling using hierarchical thresholding
F Rousselle, P Clarberg, L Leblanc, V Ostromoukhov, P Poulin
The Visual Computer 24 (7), 465-474, 2008
Improved sampling for gradient-domain metropolis light transport
M Manzi, F Rousselle, M Kettunen, J Lehtinen, M Zwicker
ACM Transactions on Graphics (TOG) 33 (6), 1-12, 2014
Kernel-predicting convolutional neural networks for denoising
T Vogels, J Novák, F Rousselle, B Mcwilliams
US Patent 10,475,165, 2019
Denoising your Monte Carlo renders: recent advances in image-space adaptive sampling and reconstruction
P Sen, M Zwicker, F Rousselle, SE Yoon, NK Kalantari
ACM Siggraph 2015 Courses, 1-255, 2015
Denoising monte carlo renderings using progressive neural networks
T Vogels, F Rousselle, B Mcwilliams, M Meyer, J Novak
US Patent 10,607,319, 2020
Denoising deep monte carlo renderings
D Vicini, D Adler, J Novák, F Rousselle, B Burley
Computer Graphics Forum 38 (1), 316-327, 2019
Predicting the reflectance of paper dyed with ink mixtures by describing light scattering as a function of ink absorbance
F Rousselle, M Hébert, R Hersch
Journal of Imaging Science and Technology 54 (5), 50501-1-50501-8, 2010
Spectral prediction model for variable dot-size printers
F Rousselle, T Bugnon, RD Hersch
Color and Imaging Conference 2008 (1), 73-78, 2008
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