We have presented an efficient adaptive sampling and reconstruction algorithm for reducing noise in Monte Carlo rendering by using Stein’s Unbiased Risk Estima-tor (SURE) in the error estimation framework. For reconstruction, the use of SURE enables us to measure the reconstruction quality for arbitrary filter kernels. It does away with the limitation of using only symmetric kernels imposed by previous work.
This freedom to use non-symmetric kernels significantly improves the effectiveness of the framework. When performing adaptive sampling, SURE can be used to de-termine the sampling density. Another contribution of this thesis is an efficient and memory-friendly approach to detect noisy geometric features when rendering depth of field and motion blur. As a result, the proposed adaptive sampling and recon-struction method efficiently eliminates MC noise while preserving the vivid details of a scene. Experiments show the proposed method offers significant improvement over state-of-the-art approaches.
One possible future direction is to implement the proposed algorithms on GPUs for interactive applications. Another possibility is to investigate some recent fast and advanced filters such as the one proposed by Gastal et al. [12]. We also would like to extend the SURE-based framework to animation rendering. In the current algorithm, as there is no built-in mechanism specifically designed for temporal data, temporal coherence cannot be guaranteed. In practice, we have experimented with a naive approach that renders each frame independently. The results look good enough
with only very subtle temporal flicking. However, a better way to handle animation would be to consider temporal samples and perform filtering in the spatial-temporal domain. Finally, it would also be interesting to adapt SURE to other rendering applications that require error estimation.
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