| 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 | 285 | 2017 |
| Neural importance sampling T Müller, B McWilliams, F Rousselle, M Gross, J Novák ACM Transactions on Graphics (ToG) 38 (5), 1-19, 2019 | 263 | 2019 |
| 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 | 193 | 2015 |
| 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 | 162 | 2018 |
| Adaptive rendering with non-local means filtering F Rousselle, C Knaus, M Zwicker ACM Transactions on Graphics (TOG) 31 (6), 1-11, 2012 | 150 | 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 | 143 | 2011 |
| Robust denoising using feature and color information F Rousselle, M Manzi, M Zwicker Computer Graphics Forum 32 (7), 121-130, 2013 | 115 | 2013 |
| 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 | 112 | 2016 |
| Real-time neural radiance caching for path tracing T Müller, F Rousselle, J Novák, A Keller arXiv preprint arXiv:2106.12372, 2021 | 68 | 2021 |
| 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 | 64 | 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 | 60 | 2015 |
| 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 | 54 | 2020 |
| Neural control variates T Müller, F Rousselle, A Keller, J Novák ACM Transactions on Graphics (TOG) 39 (6), 1-19, 2020 | 45 | 2020 |
| Kernel-predicting convolutional neural networks for denoising T Vogels, J Novák, F Rousselle, B McWilliams US Patent 10,475,165, 2019 | 43 | 2019 |
| Denoising monte carlo renderings using progressive neural networks T Vogels, F Rousselle, B McWilliams, M Meyer, J Novak US Patent 10,607,319, 2020 | 33 | 2020 |
| Image-space control variates for rendering F Rousselle, W Jarosz, J Novák ACM Transactions on Graphics (TOG) 35 (6), 1-12, 2016 | 32 | 2016 |
| NeRF‐Tex: Neural Reflectance Field Textures H Baatz, J Granskog, M Papas, F Rousselle, J Novák Computer Graphics Forum 41 (6), 287-301, 2022 | 26 | 2022 |
| 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 | 26 | 2014 |
| Compositional neural scene representations for shading inference J Granskog, F Rousselle, M Papas, J Novák ACM Transactions on Graphics (TOG) 39 (4), 135: 1-135: 13, 2020 | 21 | 2020 |
| Efficient product sampling using hierarchical thresholding F Rousselle, P Clarberg, L Leblanc, V Ostromoukhov, P Poulin The Visual Computer 24, 465-474, 2008 | 20 | 2008 |
Jan NovákPrincipal research scientist at NVIDIA ResearchVerified email at nvidia.com
