# A Data-Driven Paradigm for Precomputed Radiance Transfer

arXiv: https://arxiv.org/abs/2206.13112 · HTML: https://mlsanity.com/paper/2206.13112/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [Neural Free-Viewpoint Relighting for Glossy Indirect Illumination](https://mlsanity.com/paper/2307.06335/) (2023)

## Abstract (arXiv)

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.