Regression based Reconstruction

Our lab is designing various regression based reconstruction techniques. Some of examples include:

  • Regression based image-space filtering techniques for accelerating Monte Carlo ray tracing techniques.
  • Computing useful features guiding the regression process
  • Regression techniques for gradient-domain rendering method
  • Feature Generation for Adaptive Gradient-Domain Path Tracing
    Jonghee Back, Sung-Eui Yoon, and Bochang Moon
    Pacific Graphics(PG), 2018

    Adaptive Rendering with Linear Predictions
    Bochang Moon, Jose A. Iglesias-Guitian, Sung-Eui Yoon, Kenny Mitchell
    (Accepted at) ACM SIGGRAPH (Tran. on Graphics), 2015

    Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering
    M. Zwicker, W. Jarosz, J. Lehtinen, B. Moon, R. Ramamoorthi, F. Rousselle, P. Sen, C. Soler, and S.-E. Yoon
    State of The Art Report, EG (CGF), 2015

    Adaptive Rendering based on Weighted Local Regression
    Bochang Moon, Nathan Carr, and Sung-Eui Yoon
    Accepted in ACM Transactions on Graphics, 2014

    P-RPF: Pixel-based Random Parameter Filtering for Monte Carlo Rendering
    Hyosub Park, Bochang Moon, Soomin Kim, Sung-Eui Yoon
    CAD/Graphics (2013)

    Robust Image Denoising using a Virtual Flash Image for Monte Carlo Ray Tracing
    Bochang Moon, Jong Yun Jun, JongHyeob Lee, Kunho Kim, Toshiya Hachisuka, Sung-Eui Yoon
    Computer Graphics Forum (2013), Vol. 32, number 1, pp. 139-151.