We propose an adaptive sampling and reconstruction method for offline Monte Carlo
rendering. Our method produces sampling maps constrained by a user-defined budget that …

Recent advancements in hardware-accelerated raytracing made it possible to achieve
interactive framerates even for algorithms previously considered offline, such as path …

ReLU networks have remained the default choice for models in the area of image prediction
despite their well-established spectral bias towards learning low frequencies faster, and …

We propose a method for constructing feature sets that significantly improve the quality of
neural denoisers for Monte Carlo renderings with volumetric content. Starting from a large …

We present a kernel‐predicting neural denoising method for path‐traced deep‐Z images
that facilitates their usage in animation and visual effects production. Deep‐Z images …

Image-space denoising of rendered images has become a commonly adopted approach
since this post-rendering process often drastically reduces required sample counts (thus …

We propose a novel extension for processing frame sequences that provides context from
neighboring frames while avoiding redundant computation and a novel quantization …

Monte Carlo (MC) path tracing renders images with severe noise under one-sample-per-
pixel conditions, and one of the challenges of denoising is to achieve high rendering quality …

RELU networks have remained the default choice for models in the area of image prediction
despite their well-established spectral bias towards learning low frequencies faster, and …

We present a method for converting denoising neural networks from spatial into spatio-
temporal ones by modifying the network architecture and loss function. We insert Robust …