SURE-based Optimization for Adaptive Sampling and Reconstruction
National Taiwan University
Comparisons between greedy error minimization (GEM) [Rousselle et al. 2011] and our SURE-based filtering. With SURE, we are able to use kernels (cross bilateral filters in this case) that are more effective than GEM's isotropic Gassians. Thus, our approach better adapts to anisotropic features (such as the motion blur pattern due to the motion of the airplane) and preserves scene details (such as the textures on the floor and curtains). The kernels of both methods are visualized for comparison.
Abstract
We apply Stein's Unbiased Risk Estimator(SURE) to adaptive sampling and reconstruction to
reduce noise in Monte Carlo rendering. SURE is a general unbiased estimator for mean squared
error (MSE) in statistics. With SURE, we are able to estimate error for an arbitrary
reconstruction kernel, enabling us to use more effective kernels rather than being restricted to
the symmetric ones used in previous work. It also allows us to allocate more samples to areas with
higher estimated MSE. Adaptive sampling and reconstruction can therefore be processed within an
optimization framework. We also propose an efficient and memory-friendly approach to reduce the
impact of noisy geometry features where there is depth of field or motion blur. Experiments show
that our method produces images with less noise and crisper details than previous methods.
Publication
Tzu-Mao Li, Yu-Ting Wu, Yung-Yu Chuang. SURE-based Optimization for Adaptive Sampling and Reconstruction.
ACM Transactions on Graphics 31(6) (Proceedings of ACM SIGGRAPH Asia 2012) BibTeX
Errata
Section 5.1:
The parameters should be "sigma_fk=0.8 for normal, sigma_fk=0.25 for texture color, sigma_fk=0.6 for depth".
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- The filtering procedure is tuned so that the adaptive sampling now gives slightly better results.
- The number of samples per pixel is no longer rounded up to power of 2.
- Multiple iteration of adaptive sampling is supported.