arrow
Return

A method for estimating the errors in many-light rendering with supersampling

delete2019-06-01
delete1
delete
OA
AI
S
Sakai, Hirokazu
N
Nabata, Kosuke
Y
Yasuaki, Shinya
K
Kei Iwasaki *
DOI:10.1007/s41095-019-0137-0delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
In many-light rendering, a variety of visual and illumination effects, including anti-aliasing, depth of field, volumetric scattering, and subsurface scattering, are combined to create a number of virtual point lights (VPLs). This is done in order to simplify computation of the resulting illumination. Naive approaches that sum the direct illumination from many VPLs are computationally expensive; scalable methods can be computed more efficiently by clustering VPLs, and then estimating their sum by sampling a small number of VPLs. Although significant speed-up has been achieved using scalable methods, clustering leads to uncontrollable errors, resulting in noise in the rendered images. In this paper, we propose a method to improve the estimation accuracy of many-light rendering involving such visual and illumination effects. We demonstrate that our method can improve the estimation accuracy by a factor of 2.3 over the previous method.
Keywords:
anti-aliasing
depth of field
many-light rendering
participating media
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Computational Visual Media cover
Computational Visual Media
IF:
18.3
Papers:
310
Citations:
2.6K

Organization

Wakayama University cover
Wakayama University
Scholars:
373
Papers: 336
Citations: 190