Regression based reconstruction, 회귀 기법 기반 복원 기술, KAIST SGVR Lab

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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.