Examples for limitations of previous importance sampling methods. Previous methods produce significant noise in regions with high-frequency visibility variations due to complex occlusion or fine scene structures, such as the coffee mug handle very close to the popcorn box in LUNCH and the thin handrails in CONSTRUCTION. Insets show detailed comparisons for parts of the scenes rendered with different methods given the same amount of time (200 seconds for LUNCH and 500 seconds for CONSTRUCTION). The insets, from top to bottom, are Bidirectional Importance Sampling (BIS) [Wang and Akerlund 2009], Importance Caching (IC) [Georgiev et al. 2012], and our approach. Our method provides better noise reduction across the whole image including these difficult areas.
This paper proposes the VisibilityCluster algorithm for efficient visibility approximation and representation in many-light rendering. By carefully clustering lights and shading points, we can construct a visibility matrix that exhibits good local structures due to visibility coherence of nearby lights and shading points. Average visibility can be efficiently estimated by exploiting the sparse structure of the matrix and shooting only few shadow rays between clusters. Moreover, we can use the estimated average visibility as a quality measure for visibility estimation, enabling us to locally refine VisibilityClusters with large visibility variance for improving accuracy. We demonstrate that, with the proposed method, visibility can be incorporated into importance sampling at a reasonable cost for the many-light problem, significantly reducing variance in Monte Carlo rendering. In addition, the proposed method can be used to increase realism of local shading by adding directional occlusion effects. Experiments show that the proposed technique outperforms state-of-the-art importance sampling algorithms, and successfully enhances the preview quality for lighting design.
Yu-Ting Wu, Yung-Yu Chuang. VisibilityCluster: Average Directional Visibility for Many-Light Rendering.
IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 19, No. 9, pp. 1566-1578, September 2013.
TVCG 2013 paper, 4MB PDF