Efficient Quadrature Rules for Illumination Integrals: From Quasi Monte Carlo to Bayesian Monte Carlo (Synthesis Lectures on Computer Graphics and Animation)
暫譯: 高效的照明積分求積法則:從準蒙地卡羅到貝葉斯蒙地卡羅(計算機圖形學與動畫綜合講座)

Luís Paulo Santos, Christian Bouville, Ricardo Marques

商品描述

Rendering photorealistic images is a costly process which can take up to several days in the case of high quality images. In most cases, the task of sampling the incident radiance function to evaluate the illumination integral is responsible for an important share of the computation time. Therefore, to reach acceptable rendering times, the illumination integral must be evaluated using a limited set of samples. Such a restriction raises the question of how to obtain the most accurate approximation possible with such a limited set of samples. One must thus ensure that sampling produces the highest amount of information possible by carefully placing and weighting the limited set of samples. Furthermore, the integral evaluation should take into account not only the information brought by sampling but also possible information available prior to sampling, such as the integrand smoothness. This idea of sparse information and the need to fully exploit the little information available is present throughout this book. The presented methods correspond to the state-of-the-art solutions in computer graphics, and take into account information which had so far been underexploited (or even neglected) by the previous approaches. The intended audiences are Ph.D. students and researchers in the field of realistic image synthesis or global illumination algorithms, or any person with a solid background in graphics and numerical techniques.

商品描述(中文翻譯)

渲染照片真實感圖像是一個成本高昂的過程,對於高品質圖像來說,這個過程可能需要幾天的時間。在大多數情況下,對入射輻射函數進行取樣以評估照明積分的任務佔用了計算時間的重要部分。因此,為了達到可接受的渲染時間,必須使用有限的樣本集來評估照明積分。這樣的限制引發了如何在這樣有限的樣本集下獲得最準確的近似值的問題。因此,必須確保取樣能夠通過仔細放置和加權有限的樣本集來產生盡可能多的信息。此外,積分評估不僅應考慮取樣所帶來的信息,還應考慮取樣之前可能可用的信息,例如被積分函數的平滑性。這種稀疏信息的概念以及充分利用可用的少量信息的需求貫穿整本書。所提出的方法對應於計算機圖形學的最先進解決方案,並考慮了以往方法中未被充分利用(甚至被忽視)的信息。目標讀者是從事真實圖像合成或全局照明算法的博士生和研究人員,或任何具有堅實圖形學和數值技術背景的人士。