Proceedings: GI 2011

Sample-space bright spots removal using density estimation

Anthony Pajot , Loïc Barthe , Mathias Paulin

Proceedings of Graphics Interface 2011: St. John’s, Newfoundland, Canada, 25 - 27 May 2011, 159-166

DOI 10.20380/GI2011.21

  • Bibtex

    @inproceedings{Pajot:2011:10.20380/GI2011.21,
    author = {Pajot, Anthony and Barthe, Lo{\"i}c and Paulin, Mathias},
    title = {Sample-space bright spots removal using density estimation},
    booktitle = {Proceedings of Graphics Interface 2011},
    series = {GI 2011},
    year = {2011},
    issn = {0713-5424},
    isbn = {978-1-4503-0693-5},
    location = {St. John{\textquoteright}s, Newfoundland, Canada},
    pages = {159--166},
    numpages = {8},
    doi = {10.20380/GI2011.21},
    publisher = {Canadian Human-Computer Communications Society},
    address = {Toronto, Ontario, Canada},
    }

Abstract

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.