Adaptive sampling and reconstruction using greedy error minimization

Fabrice Rousselle, Claude Knaus, Matthias Zwicker
2011 Proceedings of the 2011 SIGGRAPH Asia Conference on - SA '11  
Figure 1 : We minimize MSE in Monte Carlo rendering by adaptive sampling and reconstruction in image space. We iterate over two steps: given current samples, optimize over a set of filters at each pixel to minimize MSE; then, given a filter at each pixel, distribute more samples to further reduce MSE. Left: initialization with 4 samples per pixel. Insets: each column is one iteration (top to bottom): filter selection (smooth filters shaded white, sharp ones black), sample density map,
more » ... tion. Right: result at an average of 32 samples per pixel. This image features single scattering participating media, indirect illumination using photon mapping, depth of field, and area lighting. Abstract We introduce a novel approach for image space adaptive sampling and reconstruction in Monte Carlo rendering. We greedily minimize relative mean squared error (MSE) by iterating over two steps. First, given a current sample distribution, we optimize over a discrete set of filters at each pixel and select the filter that minimizes the pixel error. Next, given the current filter selection, we distribute additional samples to further reduce MSE. The success of our approach hinges on a robust technique to select suitable per pixel filters. We develop a novel filter selection procedure that robustly solves this problem even with noisy input data. We evaluate our approach using effects such as motion blur, depth of field, interreflections, etc. We provide a comparison to a state-of-the-art algorithm based on wavelet shrinkage and show that we achieve significant improvements in numerical error and visual image quality. Our approach is simple to implement, requires a single user parameter, and is compatible with standard Monte Carlo rendering.
doi:10.1145/2070752.2024193 fatcat:zwytozflfzfdxnpbhdkjbkf4xm