Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Zwicker, Matthias; Jarosz, Wojciech; Lehtinen, Jaakko; Moon, Bochang; Ramamoorthi, Ravi; Rousselle, Fabrice; Sen, Pradeep; Soler, Cyril; Yoon, Sung-Eui (2015). Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering. Computer graphics forum, 34(2), pp. 667-681. Blackwell 10.1111/cgf.12592

[img]
Preview
Text
zwicker15star.pdf - Accepted Version
Available under License Publisher holds Copyright.

Download (11MB) | Preview
[img] Text
cgf12592.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (11MB) | Request a copy

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

Item Type: Journal Article (Original Article)
Division/Institute: 08 Faculty of Science > Institute of Computer Science (INF) > Computer Graphics Group (CGG)
08 Faculty of Science > Institute of Computer Science (INF)
UniBE Contributor: Zwicker, Matthias and Rousselle, Fabrice
Subjects: 000 Computer science, knowledge & systems
500 Science > 510 Mathematics
ISSN: 0167-7055
Publisher: Blackwell
Funders: [4] Swiss National Science Foundation
Projects: [UNSPECIFIED] Efficient Sampling and Reconstruction for Image Synthesis 143886
Language: English
Submitter: Matthias Zwicker
Date Deposited: 08 Jun 2016 13:03
Last Modified: 08 Jun 2016 13:03
Publisher DOI: 10.1111/cgf.12592
BORIS DOI: 10.7892/boris.81120
URI: http://boris.unibe.ch/id/eprint/81120

Actions (login required)

Edit item Edit item
Provide Feedback