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(...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 manylight 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.展开更多
基金partially supported by JSPS KAKENHI 15H05924 and 18H03348
文摘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 manylight 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.
