@techreport{freude_2020_rs, title = "R-Score: A Novel Approach to Compare Monte Carlo Renderings", author = "Christian Freude and Hiroyuki Sakai and Karoly Zsolnai-Feh\'{e}r and Michael Wimmer", year = "2020", abstract = "In this paper, we propose a new approach for the comparison and analysis of Monte Carlo (MC) rendering algorithms. It is based on a novel similarity measure called render score (RS) that is specically designed for MC rendering, statistically motivated, and incorporates bias and variance. Additionally, we propose a comparison scheme that alleviates the need for practically converged reference images (RIs). Our approach can be used to compare and analyze dierent rendering methods by revealing detailed (per-pixel) dierences and subsequently potential conceptual or implementation-related issues, thereby offering a more informative and meaningful alternative to commonly used metrics.", month = aug, number = "TR-193-02-2020-4", address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", institution = "Research Unit of Computer Graphics, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien ", note = "human contact: technical-report@cg.tuwien.ac.at", URL = "https://www.cg.tuwien.ac.at/research/publications/2020/freude_2020_rs/", } @article{CORNEL-2017-FRS, title = "Forced Random Sampling: fast generation of importance-guided blue-noise samples", author = "Daniel Cornel and Hiroyuki Sakai and Christian Luksch and Michael Wimmer", year = "2017", abstract = "In computer graphics, stochastic sampling is frequently used to efficiently approximate complex functions and integrals. The error of approximation can be reduced by distributing samples according to an importance function, but cannot be eliminated completely. To avoid visible artifacts, sample distributions are sought to be random, but spatially uniform, which is called blue-noise sampling. The generation of unbiased, importance-guided blue-noise samples is expensive and not feasible for real-time applications. Sampling algorithms for these applications focus on runtime performance at the cost of having weak blue-noise properties. Blue-noise distributions have also been proposed for digital halftoning in the form of precomputed dither matrices. Ordered dithering with such matrices allows to distribute dots with blue-noise properties according to a grayscale image. By the nature of ordered dithering, this process can be parallelized easily. We introduce a novel sampling method called forced random sampling that is based on forced random dithering, a variant of ordered dithering with blue noise. By shifting the main computational effort into the generation of a precomputed dither matrix, our sampling method runs efficiently on GPUs and allows real-time importance sampling with blue noise for a finite number of samples. We demonstrate the quality of our method in two different rendering applications.", month = jun, journal = "The Visual Computer", volume = "33", number = "6", issn = "1432-2315", pages = "833--843", keywords = "blue-noise sampling, importance sampling", URL = "https://www.cg.tuwien.ac.at/research/publications/2017/CORNEL-2017-FRS/", } @runphdthesis{sakai-thesis, title = "(unknown)", author = "Hiroyuki Sakai", year = "2015", month = nov, URL = "https://www.cg.tuwien.ac.at/research/publications/2015/sakai-thesis/", }