Currently supported:
- Iso-surface rendering with screen-space shading
- Direct Volume Rendering
- Temporal consistency and reprojection
- adaptive sampling with an importance map in screen space
- adaptive sampling in object space by changing the step size
Demonstration: https://sites.google.com/view/sebastian-weiss/research/adaptive-sampling
- renderer: a shared library exposing PyTorch operation that contains the rendering core (C++, CUDA)
- network: super-resolution network training and testing code (Python, PyTorch)
- inference-gui: interactive gui combining the renderer and networks, allows to test all available options (C++, OpenGL)
See the release page for binaries, datasets and pretrained networks
- CUDA >= 1.1
- Python >= 3.6
- PyTorch >= 1.5
- OpenGL
Tested with CUDA 10.1, Python 3.6, PyTorch 1.5, Windows 10 and Ubuntu 18