Sampling and Reconstruction Techniques for Efficient Monte Carlo Rendering
Date Issued
2014
Date
2014
Author(s)
Wu, Yu-Ting
Abstract
Two of the important tasks that computer graphics techniques try to solve are rendering photo-realistic images and performing numerically accurate simulation. Physically-based rendering can naturally satisfy these two goals. It is usually simulated by Monte Carlo ray tracing for handling a variety of sophisticated light transport paths in a unified manner. Despite its generality and simplicity, however, Monte Carlo integration converges slowly. Rendering scenes with a large number of complex geometries and realistic materials under complex illumination usually requires a large number of samples to produce a noise-free image.
In this dissertation, we proposed three advanced sampling and reconstruction algorithms for improving the performance of Monte Carlo integration. First, realizing that in complex scenes visibility is usually the major source of noise during sampling the shading function, we developed a method called VisibilityCluster for efficiently approximating visibility function. By integrating it into importance sampling framework, we achieve superior noise reduction
compared to previous approaches. Second, to reduce the computation overhead of rendering translucent materials, we proposed an algorithm, Dual-matrix sampling, to avoid evaluating unimportant surface samples which contribute
little to the final image. Finally, a general adaptive sampling and reconstruction framework named SURE-based optimization is proposed to render a wide range of distributed effects, including depth of field, motion blur, and global illumination. All of the three methods achieve significant performance improvement compared to the state-of-the-art rendering algorithms.
Subjects
三維計算機圖學
以物理為基礎的渲染法
重要性取樣法
半透明物質渲染法
自適應採樣與重建技術
Type
thesis
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