Tutkimustuotoksia vuodessa
Tutkimustuotoksia vuodessa
Description
Pre-rendered data for training and evaluating the Neural Reconstruction for Gradient-Domain Path Tracing ("NGPT") algorithm described in the paper Deep Convolutional Reconstruction for Gradient-Domain Rendering.
See https://github.com/mkettune/ngpt/.
Download 'all_datasets.zip' for easy access to all of the data. Alternatively, you may download the datasets one by one.
The displayed license (CC BY 4.0) covers the rendering and collection work. Copyrights of the individual scenes may need to be respected as well and are displayed in file 'LICENSE'.
See https://github.com/mkettune/ngpt/.
Download 'all_datasets.zip' for easy access to all of the data. Alternatively, you may download the datasets one by one.
The displayed license (CC BY 4.0) covers the rendering and collection work. Copyrights of the individual scenes may need to be respected as well and are displayed in file 'LICENSE'.
| Koska saatavilla | 20 jouluk. 2019 |
|---|---|
| Julkaisija | Zenodo |
Dataset Licences
- CC-BY-4.0
Tutkimustuotos
- 1 Article
-
Deep Convolutional Reconstruction For Gradient-Domain Rendering
Kettunen, M., Harkonen, E. & Lehtinen, J., heinäk. 2019, julkaisussa: ACM Transactions on Graphics. 38, 4, 12 Sivumäärä, 126.Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu
Open accessTiedosto55 Lataukset (Pure)
Projektit
- 1 Päättynyt
-
Building Smoothly Converging Unbiased Rendering Algorithms - Robustit, tilastollisesti harhattomat kuvasynteesialgoritmit
Lehtinen, J., Silvennoinen, A., Aittala, M. & Kettunen, M.
01/09/2014 → 31/08/2018
Projekti: Academy of Finland: Other research funding