Matthias Zwicker University of Bern Conclusions. Properties of effective filters Exploit auxiliary information from renderer Per-pixel features (normal,

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Presentation transcript:

Matthias Zwicker University of Bern Conclusions

Properties of effective filters Exploit auxiliary information from renderer Per-pixel features (normal, position, albedo, etc.) Support complex filter shapes Joint bilateral, NL-means filter Weighted local regression Use per-pixel filter parameters Use input variance Predict using mutual information, learning Estimate error of filter output (SURE, bias and variance) 2

Industry adoption Pixar RenderMan Disney Hyperion innoBright 3

Open challenges Real-time applications Animation sequences Beyond image space filtering Exploit additional path space properties Leverage theoretical foundations Sampling theory Learning-based techniques Sparse methods Theoretical analysis, proof of lower bounds on sampling density 4

Resources Survey paper ”Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering”, Computer Graphics Forum, 2015 Source code, partial list Rousselle et al. [RKZ11]: auxiliary.zip/at_download/filehttp:// auxiliary.zip/at_download/file Rousselle et al. [RKZ12]: data.zip/at_download/file data.zip/at_download/file Sen and Darabi [SD12]: /F4MW2F28http://dx.doi.org/ /F4MW2F28 Kalantari and Sen [KS13]: Stuff/RemovingMCNoise_ v1.0.zip Stuff/RemovingMCNoise_ v1.0.zip Moon et al. [MJL ∗ 13]: Rousselle et al. [RMZ13]: wnload/file wnload/file Moon et al. [MCY14]: 5

Thank you! Funding agencies and partners  NSF, Intel, Nvidia, SNSF, NRF Co-authors 6 Matthias Zwicker University of Bern Pradeep Sen UC Santa Barbara Fabrice Rousselle Disney Research Nima Kalantari UC Santa Barbara Sung-Eui Yoon KAIST Course organizers and presenters: