论文标题
用于预先计算的辐射转移的数据驱动范例
A Data-Driven Paradigm for Precomputed Radiance Transfer
论文作者
论文摘要
在这项工作中，我们探讨了以数据驱动方式构建预先计算的辐射转移（PRT）方法的更改。这种范式的转变使我们能够减轻构建传统PRT方法的困难，例如定义重建基础，编码专用路径示踪剂来计算传输功能，等等。我们的目标是通过提供简单的基线算法来为机器学习的方法铺平道路。更具体地说，我们通过一些直接照明测量值展示了头发和表面中间接照明的实时渲染。我们仅使用诸如单数值分解（SVD）等标准工具（SVD）来提取重建基础和转移功能等标准工具，从直接和间接照明渲染对构建基线。
In this work, we explore a change of paradigm to build Precomputed Radiance Transfer (PRT) methods in a data-driven way. This paradigm shift allows us to alleviate the difficulties of building traditional PRT methods such as defining a reconstruction basis, coding a dedicated path tracer to compute a transfer function, etc. Our objective is to pave the way for Machine Learned methods by providing a simple baseline algorithm. More specifically, we demonstrate real-time rendering of indirect illumination in hair and surfaces from a few measurements of direct lighting. We build our baseline from pairs of direct and indirect illumination renderings using only standard tools such as Singular Value Decomposition (SVD) to extract both the reconstruction basis and transfer function.
