A Data-Driven Paradigm for Precomputed Radiance Transfer - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year :

A Data-Driven Paradigm for Precomputed Radiance Transfer


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.
Vignette du fichier
data-driven_paradigm--thumbnail.png (426.59 Ko) Télécharger le fichier Fichier principal
Vignette du fichier
Data_driven_paradigm.pdf (29.59 Mo) Télécharger le fichier
Format : Figure, Image

Dates and versions

hal-03726655 , version 1 (18-07-2022)



Laurent Belcour, Thomas Deliot, Wilhem Barbier, Cyril Soler. A Data-Driven Paradigm for Precomputed Radiance Transfer. SIGGRAPH 2022 - Conference & Exhibition on Computer Graphics & Interactive Techniques, Aug 2022, Vancouver, Canada. pp.1-8. ⟨hal-03726655⟩
105 View
19 Download



Gmail Facebook Twitter LinkedIn More