Deep Adaptive Sampling for Low Sample Count Rendering
| dc.contributor.author | Kuznetsov, Alexandr | en_US |
| dc.contributor.author | Kalantari, Nima Khademi | en_US |
| dc.contributor.author | Ramamoorthi, Ravi | en_US |
| dc.contributor.editor | Jakob, Wenzel and Hachisuka, Toshiya | en_US |
| dc.date.accessioned | 2018-07-01T07:22:33Z | |
| dc.date.available | 2018-07-01T07:22:33Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Recently, deep learning approaches have proven successful at removing noise from Monte Carlo (MC) rendered images at extremely low sampling rates, e.g., 1-4 samples per pixel (spp). While these methods provide dramatic speedups, they operate on uniformly sampled MC rendered images. However, the full promise of low sample counts requires both adaptive sampling and reconstruction/denoising. Unfortunately, the traditional adaptive sampling techniques fail to handle the cases with low sampling rates, since there is insufficient information to reliably calculate their required features, such as variance and contrast. In this paper, we address this issue by proposing a deep learning approach for joint adaptive sampling and reconstruction of MC rendered images with extremely low sample counts. Our system consists of two convolutional neural networks (CNN), responsible for estimating the sampling map and denoising, separated by a renderer. Specifically, we first render a scene with one spp and then use the first CNN to estimate a sampling map, which is used to distribute three additional samples per pixel on average adaptively. We then filter the resulting render with the second CNN to produce the final denoised image. We train both networks by minimizing the error between the denoised and ground truth images on a set of training scenes. To use backpropagation for training both networks, we propose an approach to effectively compute the gradient of the renderer. We demonstrate that our approach produces better results compared to other sampling techniques. On average, our 4 spp renders are comparable to 6 spp from uniform sampling with deep learning-based denoising. Therefore, 50% more uniformly distributed samples are required to achieve equal quality without adaptive sampling. | en_US |
| dc.description.number | 4 | |
| dc.description.sectionheaders | Sampling | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13473 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 35-44 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13473 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13473 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Ray tracing | |
| dc.subject | Neural networks | |
| dc.subject | Image processing | |
| dc.title | Deep Adaptive Sampling for Low Sample Count Rendering | en_US |