Rendering

Anamorphosis for 2D displays can provide viewer centric perspective viewing, enabling 3D appearance, eye contact and engagement, by adapting dynamically in real time to a single moving viewer’s viewpoint, but at the cost of distorted viewing for other viewers. We present a method for constructing non-linear projections as a combination of anamorphic rendering of selective objects whilst reverting to normal perspective rendering of the rest of the scene. Our study defines a scene consisting of five characters, with one of these characters selectively rendered in anamorphic perspective.

We present a modular convolutional architecture for denoising rendered images.

We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks.

We investigate how to generalize this concept to non-invertible sampling techniques commonly found in practice, and introduce probabilistic inverses that extend our perturbation to cover most sampling methods found in light transport simulations.

The interactive narrative guides guests through the immersive story with lighting and spatial audio design and integrates both walkable and air haptic actuators.

We present two novel unbiased techniques for sampling free paths in heterogeneous participating media.

We introduce a deep learning approach for denoising Monte Carlo-rendered images that produces high-quality results suitable for production.

We address the challenge of efficiently rendering massive assemblies of grains within a forward path-tracing framework.

We propose an image-space (iterative) reconstruction scheme that employs control variates to reduce variance.

We propose a new real-time temporal filtering and antialiasing (AA) method for rasterization graphics pipelines.