Research output per year
Research output per year
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'.
| Date made available | 20 Dec 2019 |
|---|---|
| Publisher | Zenodo |
Dataset Licences
- CC-BY-4.0
Research output
- 1 Article
-
Deep Convolutional Reconstruction For Gradient-Domain Rendering
Kettunen, M., Harkonen, E. & Lehtinen, J., Jul 2019, In: ACM Transactions on Graphics. 38, 4, 12 p., 126.Research output: Contribution to journal › Article › Scientific › peer-review
Open Access76 Downloads (Pure)
Projects
- 1 Finished
-
Robust Unbiased Image Synthesis
Lehtinen, J. (Principal investigator), Silvennoinen, A. (Project Member), Kettunen, M. (Project Member) & Aittala, M. (Project Member)
01/09/2014 → 31/08/2018
Project: Academy of Finland: Other research funding