4.6 Article

Binary space partitioning visibility tree for polygonal and environment light rendering

期刊

VISUAL COMPUTER
卷 37, 期 9-11, 页码 2499-2511

出版社

SPRINGER
DOI: 10.1007/s00371-021-02181-8

关键词

Polygonal light; GGX BRDF; Visibility

资金

  1. JSPS KAKENHI [18H03348, 21H03571]
  2. Grants-in-Aid for Scientific Research [21H03571, 18H03348] Funding Source: KAKEN

向作者/读者索取更多资源

This paper introduces a geometric approach to render shadows for physically based materials under polygonal light sources, utilizing a hierarchical representation for the precomputed visibility function to retain the advantages of closed-form solutions. Experimental results demonstrate that the method can render complex shadows with a GGX microfacet BRDF from polygonal light sources at interactive frame rates, and it can be easily incorporated into environment lighting.
In this paper, we present a geometric approach to render shadows for physically based materials under polygonal light sources. Direct illumination calculation from a polygonal light source involves the triple product integral of the lighting, the bidirectional reflectance distribution function (BRDF), and the visibility function over the polygonal domain, which is computation intensive. To achieve real-time performance, work on polygonal light shading exploits analytical solutions of boundary integrals along the edges of the polygonal light at the cost of lacking shadowing effects. We introduce a hierarchical representation for the precomputed visibility function to retain the merits of closed-form solutions for boundary integrals. Our method subdivides the polygonal light into a set of polygons visible from a point to be shaded. Experimental results show that our method can render complex shadows with a GGX microfacet BRDF from polygonal light sources at interactive frame rates. In addition, our visibility representation can be easily incorporated into environment lighting.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据