Christoph Schied

TL;DR: A versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations is introduced, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of 1920×1080.

TL;DR: This work proposes a variant of deep convolutional networks better suited to the class of noise present in Monte Carlo rendering, which allows for much larger pixel neighborhoods to be taken into account, while also improving execution speed by an order of magnitude.

TL;DR: A reconstruction algorithm is introduced that generates a temporally stable sequence of images from one path-per-pixel global illumination, using temporal accumulation to increase the effective sample count and spatiotemporal luminance variance estimates to drive a hierarchical, image-space wavelet filter.

TL;DR: A novel temporal filter which analyzes the signal over time to derive adaptive temporal accumulation factors per pixel and repurposes a subset of the shading budget to sparsely sample and reconstruct the temporal gradient, showing a significant reduction of lag and ghosting as well as improved temporal stability.

TL;DR: This course will take a look at how far out the future is, review the state of the art, and identify the current challenges for research.