Search results for Jan Novák

We present a robust, unbiased technique for intelligent light-path construction in path-tracing algorithms.

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

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 practical way to precompute and compactly store ASOs and demonstrate their ability to accelerate path tracing.

We present a coherent survey of methods that utilize Monte Carlo integration for estimating light transport in scenes containing participating media.

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

We present a modular convolutional architecture for denoising rendered images.

We present a novel algorithm to denoise deep Monte Carlo renderings, in which pixels contain multiple color values, each for a different range of depths.