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Screen Space Ambient Occlusion Based Multiple Importance Sampling for Real-Time Rendering

Screen Space Ambient Occlusion Based Multiple Importance Sampling for Real-Time Rendering We propose a new approximation technique for accelerating the Global Illumination algorithm for real-time rendering. The proposed approach is based on the Screen-Space Ambient Occlusion (SSAO) method, which approximates the global illumination for large, fully dynamic scenes at interactive frame rates. Current algorithms that are based on the SSAO method suffer from difficulties due to the large number of samples that are required. In this paper, we propose an improvement to the SSAO technique by integrating it with a Multiple Importance Sampling technique that combines a stratified sampling method with an importance sampling method, with the objective of reducing the number of samples. Experimental evaluation demonstrates that our technique can produce high-quality images in real time and is significantly faster than traditional techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png 3D Research Springer Journals

Screen Space Ambient Occlusion Based Multiple Importance Sampling for Real-Time Rendering

Zerari, Abd; Babahenini, Mohamed
3D Research , Volume 9 (1) – Dec 14, 2017
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References (27)

Publisher
Springer Journals
Copyright
Copyright © 2017 by 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Signal,Image and Speech Processing; Computer Imaging, Vision, Pattern Recognition and Graphics; Optics, Lasers, Photonics, Optical Devices
eISSN
2092-6731
DOI
10.1007/s13319-017-0152-9
Publisher site
See Article on Publisher Site

Abstract

We propose a new approximation technique for accelerating the Global Illumination algorithm for real-time rendering. The proposed approach is based on the Screen-Space Ambient Occlusion (SSAO) method, which approximates the global illumination for large, fully dynamic scenes at interactive frame rates. Current algorithms that are based on the SSAO method suffer from difficulties due to the large number of samples that are required. In this paper, we propose an improvement to the SSAO technique by integrating it with a Multiple Importance Sampling technique that combines a stratified sampling method with an importance sampling method, with the objective of reducing the number of samples. Experimental evaluation demonstrates that our technique can produce high-quality images in real time and is significantly faster than traditional techniques.

Journal

3D Research – Springer Journals

Published: Dec 14, 2017

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