分块采样路径空间，用引导采样提升渲染效率与质量
Path Space Partitioning and Guided Image Sampling for MCMC
- 将路径空间按标准蒙特卡洛分析结果分割，分块用MCMC估计
- 相同采样数下，图像质量优于其他MCMC方法
- 适合对渲染精度要求高的实时或离线图形应用
渲染算法通常在统一的路径空间中积分光路。然而，对这一整体空间进行积分未必是最高效的方法。本文表明，对路径空间进行划分，并对每个分区使用独立估计器进行积分，可带来优势。我们提出一种基于标准蒙特卡洛估计器分析路径的路径空间划分方法，并采用马尔可夫链蒙特卡洛（MCMC）估计器对这些分区进行积分。这使得积分仅在路径空间的稀疏子集内进行，因此我们引入图像空间中的引导提议分布以提高效率。实验显示，在相同采样数量下，该方法相比其他MCMC集成方式能显著提升图像质量。
原文摘要 · Abstract (English)
Rendering algorithms typically integrate light paths over path space. However, integrating over this one unified space is not necessarily the most efficient approach, and we show that partitioning path space and integrating each of these partitioned spaces with a separate estimator can have advantages. We propose an approach for partitioning path space based on analyzing paths from a standard Monte Carlo estimator and integrating these partitioned path spaces using a Markov Chain Monte Carlo (MCMC) estimator. This also means that integration happens within a sparser subset of path space, so we propose the use of guided proposal distributions in image space to improve efficiency. We show that our method improves image quality over other MCMC integration approaches at the same number of samples.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。