Jan Novák
Practical Path Guiding for Efficient Light-Transport Simulation
We present a robust, unbiased technique for intelligent light-path construction in path-tracing algorithms.
Spectral and Decomposition Tracking for Rendering Heterogeneous Volumes
We present two novel unbiased techniques for sampling free paths in heterogeneous participating media.
Efficient Rendering of Heterogeneous Poly-Disperse Granular Media
We address the challenge of efficiently rendering massive assemblies of grains within a forward path-tracing framework.
Image-Space Control Variates for Rendering
We propose an image-space (iterative) reconstruction scheme that employs control variates to reduce variance.
Reduced Aggregate Scattering Operators for Path Tracing
We propose a practical way to precompute and compactly store ASOs and demonstrate their ability to accelerate path tracing.
Monte Carlo Methods for Volumetric Light Transport Simulation
We present a coherent survey of methods that utilize Monte Carlo integration for estimating light transport in scenes containing participating media.
Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings
We introduce a deep learning approach for denoising Monte Carlo-rendered images that produces high-quality results suitable for production.
Denoising with Kernel Prediction and Asymmetric Loss Functions
We present a modular convolutional architecture for denoising rendered images.
Denoising Deep Monte Carlo Renderings
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.
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