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标题: Adaptive Sampling and Reconstruction using Greedy Error Minimization [打印本页]

作者: 晃晃 时间: 2011-12-28 10:55
标题: Adaptive Sampling and Reconstruction using Greedy Error Minimization
Adaptive Sampling and Recons***ction using Greedy Error Minimization

Fabrice Rousselle Claude Knaus y Matthias Zwicker

University of Bern University of Bern University of Bern

Abstract

We introduce a novel approach for image space adaptive sampling

and recons***ction in Monte Carlo rendering. We greedily mini-

mize relative mean squared error (MSE) by iterating over two steps.

First, given a current sample distribution, we optimize over a dis-

crete set of ﬁlters at each pixel and select the ﬁlter that minimizes

the pixel error. Next, given the current ﬁlter selection, we distribute

additional samples to further reduce MSE. The success of our ap-

proach hinges on a robust technique to select suitable per pixel ﬁl-

ters. We develop a novel ﬁlter selection procedure that robustly

solves this problem even with noisy input data. We evaluate our ap-

proach using effects such as motion blur, depth of ﬁeld, interreﬂec-

tions, etc. We provide a comparison to a state-of-the-art algorithm

based on wavelet shrinkage and show that we achieve signiﬁcant

improvements in numerical error and visual image quality. Our ap-

proach is simple to implement, requires a single user parameter, and

is compatible with standard Monte Carlo rendering.

CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional

Graphics and Realism—Raytracing;

Keywords: adaptive sampling and recons***ction

1 Introduction

Monte Carlo techniques compute pixel colors by (quasi-)randomly

sampling an integration domain that covers all light paths trans-

porting light from a source to the camera. The integration domain

may include effects such as depth of ﬁeld, motion blur, and light

paths with multiple interreﬂections. Unless one computes an exces-

sive number of samples, this often leads to high pixel variance and

the typical noise artifacts in Monte Carlo rendering. There are two

main strategies to address this. The ﬁrst is to distribute samples in

an optimal fashion, with respect to the problem at hand. The second

is to smooth out noise by applying suitable ﬁlters. Both strategies

can be applied in the high dimensional space of light paths or in

the image plane. We focus on strategies that operate in the image

plane.

We formulate the problem as follows: given a certain budget of

Monte Carlo samples, obtain an image that minimizes the relative

mean squared error (MSE) by distributing samples in a suitable

fashion in the image plane and by ﬁltering the image with appro-

priate ﬁlters. We can interpret this as an optimization problem over

the space of sample distributions and image ﬁlters. Our core idea is

to make the problem tractable by restricting the space of ﬁlters to a

discrete set of predetermined ﬁlters per pixel. Each pixel may have

a different set of ﬁlters, but the set is predeﬁned and not itself part

of the optimization. We use a simple greedy strategy to obtain an

approximate solution to the MSE minimization problem. Starting

from an initial set of samples, we iterate over two steps. First, for

each pixel we select the ﬁlter from its discrete set that minimizes the

pixel MSE given the current samples. Second, given the currently

chosen pixel ﬁlters, we distribute a new batch of samples that try to

further reduce MSE as much as possible. This process is repeated

until a termination criterion is fulﬁlled.

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作者: 菜刀吻电线 时间: 2012-2-8 23:28
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作者: 菜刀吻电线 时间: 2012-5-3 23:25
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作者: C.R.CAN 时间: 2012-5-31 23:33
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作者: 菜刀吻电线 时间: 2012-7-15 23:22
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作者: 晃晃 时间: 2012-10-15 23:24
其实楼主所说的这些，俺支很少用！

作者: 菜刀吻电线 时间: 2012-10-29 23:35
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作者: 奇 时间: 2013-1-31 23:24
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