Batch Specular Manifold Sampling for caustics rendering

Pengpei Hong, Chuhua Xian, Hongmin Cai, Jiazhou Chen, Guiqing Li

Abstract: Caustics rendering has long posed a formidable challenge within the realm of light transport simulation. In a recent breakthrough, Zeltner introduced Specular Manifold Sampling (SMS), a technique adept at unbiasedly addressing caustics within the traditional Monte Carlo framework. SMS ingeniously employs the Bernoulli experiment to assess the inverse probability of sampled specular paths. However, the intricacies of complex scene geometries can render this process computationally intensive and the resulting estimations less than ideally accurate. To address this problem, this paper presents an innovative method designed to expedite the estimation process and bolster its precision. Our approach cleverly allocates Bernoulli trials among diverse specular solutions, effectively spreading the computational burden and enhancing the sampling rate for both the shading point and individual solutions. This optimization significantly refines the accuracy of the inverse probability estimation. Empirical results demonstrate that our proposed method not only surpasses SMS but also achieves a marked reduction in the variance associated with caustics rendering.

External IDs:dblp:journals/vc/HongXCCL25