Efficient Depth-of-Field Rendering with Adaptive Sampling and Multiscale Reconstruction

被引:12
作者
Chen, Jiating [1 ,2 ,3 ,4 ]
Wang, Bin [1 ,3 ,4 ]
Wang, Yuxiang [1 ,2 ,3 ,4 ]
Overbeck, Ryan S. [5 ]
Yong, Jun-Hai [1 ,3 ,4 ]
Wang, Wenping [6 ]
机构
[1] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China
[3] Minist Educ China, Key Lab Informat Syst Secur, Beijing, Peoples R China
[4] Tsinghua Natl Lab Informat Sci & Technol, Beijing, Peoples R China
[5] Columbia Univ, New York, NY 10027 USA
[6] Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
基金
美国国家科学基金会;
关键词
depth-of-field rendering; adaptive sampling; multiscale reconstruction;
D O I
10.1111/j.1467-8659.2011.01854.x
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Depth-of-field is one of the most crucial rendering effects for synthesizing photorealistic images. Unfortunately, this effect is also extremely costly. It can take hundreds to thousands of samples to achieve noise-free results using Monte Carlo integration. This paper introduces an efficient adaptive depth-of-field rendering algorithm that achieves noise-free results using significantly fewer samples. Our algorithm consists of two main phases: adaptive sampling and image reconstruction. In the adaptive sampling phase, the adaptive sample density is determined by a 'blur-size' map and 'pixel-variance' map computed in the initialization. In the image reconstruction phase, based on the blur-size map, we use a novel multiscale reconstruction filter to dramatically reduce the noise in the defocused areas where the sampled radiance has high variance. Because of the efficiency of this new filter, only a few samples are required. With the combination of the adaptive sampler and the multiscale filter, our algorithm renders near-reference quality depth-of-field images with significantly fewer samples than previous techniques.
引用
收藏
页码:1667 / 1680
页数:14
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