Boosting histogram-based denoising methods with gpu optimizations

Abstract

We present a system which allows for guiding the image quality in global illumination (GI) methods by user-specified regions of interest (ROIs). This is done with either a tracked interaction device or a mouse-based method, making it possible to create a visualization with varying convergence rates throughout one image towards a GI solution. To achieve this, we introduce a scheduling approach based on Sparse Matrix Compression (SMC) for efficient generation and distribution of rendering tasks on the GPU that allows for altering the sampling density over the image plane. Moreover, we present a prototypical approach for filtering the newly, possibly sparse samples to a final image. Finally, we show how large-scale display systems can benefit from rendering with ROIs.