SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
Energy preserving non-linear filters
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Global illumination using photon maps
Proceedings of the eurographics workshop on Rendering techniques '96
Anisotropic diffusion for Monte Carlo noise reduction
ACM Transactions on Graphics (TOG)
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Robust monte carlo methods for light transport simulation
Robust monte carlo methods for light transport simulation
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A Novel Monte Carlo Noise Reduction Operator
IEEE Computer Graphics and Applications
Edge-avoiding À-Trous wavelet transform for fast global illumination filtering
Proceedings of the Conference on High Performance Graphics
Effective despeckling of HDR images
SIGGRAPH Asia 2011 Sketches
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Rendering images using Monte-Carlo estimation is prone to bright spots artefacts. Bright spots correspond to high intensity pixels that appear when a very low probability sample outweighs all other sample contributions. We present an average estimator that is robust to outliers, which detects and removes samples that are considered as outliers, and lead to bright spots in images computed using Monte-Carlo estimation. By progressively building a per-pixel representation of the luminance distribution, our method is able to delay samples whose luminance is considered as an outlier with respect to the current distribution. This distribution is continuously updated so that delayed samples may be re-considered as viable later in the rendering process, thus making the presented approach robust. Our method does not suffer from blurring in high-frequency zones. It can be easily integrated in any Monte-Carlo-based rendering system, used in conjunction with any adaptive sampling scheme, and it introduces a very small computational overhead, which is negligible compared to the use of over-sampling.