General and Robust Error Estimation and Reconstruction for Monte Carlo Rendering

Figure 1: A complex scene with fine details and global illumination. Left: Images rendered with PBRT [PH10] using 32 samples per pixel rendered in 2.5 minutes. Middle: Image reconstructed by our algorithm in 2.6 minutes including rendering and filtering. Right: Equal error image with 200 samples per pixel rendered in 12.7 minutes. Adaptive filtering techniques have proven successful in handling non-uniform noise in Monte-Carlo rendering approaches. A recent trend is to choose an optimal filter per pixel from a selection of non spatially-varying filters. Nonetheless, the best filter choice is difficult to predict in the absence of a reference rendering. Our approach relies on the observation that the reconstruction error is locally smooth for a given filter. Hence, we propose to construct a dense error prediction from a small set of sparse but robust estimates. The filter selection is then formulated as a non-local optimization problem, which we solve via graph cuts, to avoid visual artifacts due to inconsistent filter choices. Our approach does not impose any restrictions on the used filters, outperforms previous state-of-the-art techniques and provides an extensible framework for future reconstruction techniques