We present a method for reducing errors---variance and bias---in physically based
differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as …

We propose neural control variates (NCV) for unbiased variance reduction in parametric
Monte Carlo integration. So far, the core challenge of applying the method of control variates …

This thesis develops a generalized form of Monte Carlo integration called Resampled
Importance Sampling. It is based on the importance resampling sample generation …

Image-based representations for illumination can capture complex real-world lighting that is
difficult to represent in other forms. Current importance sampling strategies for image-based …

We explore the theory of integration with control variates in the context of rendering. Our
goal is to optimally combine multiple estimators using their covariances. We focus on two …

We present an interactive system for fully dynamic scene lighting using captured high
dynamic range (HDR) video environment maps. The key component of our system is an …

The visibility function in direct illumination describes the binary visibility over a light source,
eg, an environment map. Intuitively, the visibility is often strongly correlated between nearby …

Many algorithms, such as level of detail rendering and occlusion culling methods, make
decisions based on the degree of visibility of an object, but do not analyze the distribution, or …

Image-based representations for illumination are able to capture complex real-world lighting
that is difficult to represent in other forms. To date, the direction of most work for sampling …

Abstract We present the Optimizing Control Variate (OCV) estimator, a new estimator for
Monte Carlo rendering. Based upon a deterministic sampling framework, OCV allows …